Cross-media usage and explorative digital music consumption: An optimum stimulation-level perspective and evidence from China

Abstract

Based on a theoretical perspective of the optimum stimulation level (OSL), we investigate how the cross-media usage (multi-device and multi-app usage) of individual users influences their exploration of new music in a music-streaming platform. We also examine whether gender moderates the link between cross-media usage and new music exploration behavior. We analyze survey responses from 1116 college students in China, and our findings show a significant and positive effect of multi-device usage on new music exploration. By contrast, multi-app usage does not have any statistically significant effect. The results also indicate that being a woman positively moderates the relationship between multi-app usage and new music exploration behavior. Our study contributes to the understanding of exploratory user behavior in a new media context by linking the OSL theory to digital music consumption.

Keywords

Cross-media explorative consumption gender mobile app multi-app multi-device music industry new media optimum stimulation level (OSL)

Introduction

Consumption behaviors are closely related to technological changes. Specifically, new media have dramatically changed the market for cultural and creative products, thus allowing new creators to enter the marketplaces and the users to have access to more diverse options (Ryu and Park, 2018; Tepper and Hargittai, 2009). For example, in the music industry, online marketplaces, such as Amazon, offer more physical products, for example, CDs, than physical stores offer. Moreover, digital download and streaming platforms, such as iTunes and Spotify, offer their users millions of unique song tracks. With the advancements in new media, the business model of the music industry has been completely transformed (Meier and Manzerolle, 2018). Users now avail myriad devices and services that allow them to access and listen to music in novel ways. Traditional economic arrangements surrounding the music industry have been disrupted because the industry has undergone a digital transformation (Bhattacharjee et al., 2003; Yoo et al., 2010). For example, many users listen to music using different music services and cross-media environments, for example, multiple devices and multiple digital platforms (Kim, 2014). Users can also actively create their own listening experience by using new technologies and new media channels, such as community listening and music discovery based on algorithmic recommendations (Glitsos, 2019).

In this regard, the impact of new media on music consumption has been an enticing subject that has attracted considerable attention from researchers as well as industry experts from different domains (Clement et al., 2012; Dewan et al., 2017; Dewan and Ramaprasad, 2014; Hesmondhalgh, 2021; Papies et al., 2011; Warr and Goode, 2011). The extant research has expanded our understanding of how digitalization has changed the overall music consumption behavior (Danckwerts and Kenning, 2019; Glitsos, 2019; Mead et al., 2019; Webster, 2021). However, little is known about how digital music consumption through new media has influenced the way in which users explore new music and update their consumption sets. In particular, there has been no systematic study that has explored how cross-media usage impacts music consumption along different dimensions of music exploration, such as novelty-seeking (Hirschman, 1980), variety-seeking (Kahn, 1995; Menon and Kahn, 1995; Roehm and Roehm, 2005), and curiosity-motivated information-seeking (Helm and Landschulze, 2009; Koo and Ju, 2010). This aspect has important implications for both academicians and practitioners. Understanding an individual user’s propensity for explorative consumption is a topic of critical interest because such a behavior shapes the user’s responses to different stimuli in diverse settings (Jayarajah et al., 2014).

The theory of optimum stimulation level (OSL) has been extensively employed as a theoretical lens for studying the differences in exploratory behavior of individuals, effects of stimulation, and consumer behavior in information searching (Helm and Landschulze, 2009; Raju, 1980). OSL is defined as the ideal level of stimulation that an individual prefers and its relation to the individual (Raju, 1980). When an environment provides a lower level of stimulation than an optimal level, the individual seeks increased stimulation, which leads to an explorative behavior, for example, novelty-seeking or variety-seeking. Conversely, if the environment provides a higher level of stimulation than the optimal level, the individual pursues more moderate situations by avoiding novelty or variety. The existing literature regards different exploratory consumption behaviors, for example, novelty-seeking, variety-seeking, brand switching, and risk-taking, as reflections of the exploratory tendencies of the consumers, that is, a high OSL. Moreover, the OSL is a function of personal traits and characteristics, such as personality and demographic variables. It indicates that applying the theory in different contexts can reveal interesting nuances (Baumgartner and Steenkamp, 1996; Steenkamp and Burgess, 2002).

In this study, we focus on the explorative user behavior for digital music consumption. Based on the theoretical perspective of the OSL, we hypothesize that different cross-media usage, including multi-device and multi-app usage, leads to differences in the explorative behavior of the consumers. Furthermore, we explore how gender moderates this relationship, given that gender differences systematically affect exploratory consumer behavior (Steenkamp and Burgess, 2002; Zuckerman and Kuhlman, 2000) and attitudes toward music and new media adoption and the use of digital music channels (Colley, 2008; North et al., 2000; O’Neill, 1997). Thus, we investigate the following hypothetical research questions:

How does the cross-media usage by individual users, as a reflection of their explorative tendencies, influence their exploration of new music?

Can gender difference strengthen or weaken the link between their cross-media usage and new music exploration behavior?

To answer these questions, we choose young Chinese consumers as our research subjects. In terms of revenue from digital music, China was second to the United States, with 2 billion USD in sales as of 2020.¹ The number of Chinese users who listen to music online has dramatically increased over the last decade. As one of the driving factors, Chinese music-streaming platforms are free. Unlike global counterparts such as Spotify and Apple Music, which rely heavily on user subscriptions for revenue, Chinese digital music platforms provide basic music-streaming services for free and generate revenue from different sources, including advertisements, live streaming, concert ticket sales, and paid memberships. Each platform offers unique content from different artists, and thus, using multiple apps and switching between them are common for users. The younger generations are the most active digital music platform users, with most of them accessing these services on mobile devices such as smartphones (Nielsen, 2020).

We surveyed the digital music consumption of college students in major universities of China. The survey comprised a questionnaire on aspects such as listening to music, music exploration, and music payment behaviors. Overall, we collected responses from 1116 participants. The results from the analysis of the survey responses revealed a significantly positive effect of multi-device usage on new music exploration, whereas there was no statistically significant effect of multi-app usage. The findings also show that gender moderates the relationship between multi-app usage and music exploration behavior, but not between multi-device usage and music exploration behavior. Although our findings expand our understanding of music exploration behavior in a digital environment, the implications are more applicable to the Chinese context. New media usage and gender nuances may vary across cultures (Campbell, 2007; Li and Kirkup, 2007; Westlund, 2010).

Our study contributes to expanding our understanding of exploratory user behavior in the new media context by linking the OSL theory to digital music consumption (Adomavicius et al., 2015). The new environment offers numerous cross-media alternatives and choices for discovery, thus generating diverse avenues for understanding digital goods consumption dynamics (Kim, 2014). Although the OSL theory has been extensively examined in psychology and marketing literature (Lin and Lin, 2012; Raju, 1980; Steenkamp and Burgess, 2002), it has not been rigorously investigated in the digital context. Our study aims to close the gap by applying the OSL theory to demonstrate that the intrinsic drivers of explorative behaviors are stable in the digital context (Helm and Landschulze, 2009). Understanding the explorative behavior in digital music consumption will also provide profound insights for practitioners in the new media domains.

Related literature

Technological advance in music industry

Since the 1990s, new technological advances such as digitization, data compression, and the Internet have changed music production, distribution, and consumption (Datta et al., 2017; Gopal et al., 2004; Im et al., 2019; Tepper and Hargittai, 2009). For example, the advent of the Internet and the MP3 format has enabled (illegal) peer-to-peer music sharing (Bhattacharjee et al., 2003; Lam and Tan, 2001). The downloadable MP3 format can be shared over the Internet and often replaces traditional media, including CDs. Digital music piracy has dramatically reduced the sales of physical albums, one of the major revenue sources for record labels, leading to a decline in revenue of the global record industry. To address this problem, different digital music services have emerged, including online download services (e.g. iTunes), ad-based online streaming (e.g. Pandora), and paid-subscription streaming services such as Spotify and Apple Music (Im et al., 2019; Meier and Manzerolle, 2018).

Technological advances in the music industry have always been an area of interest in various disciplines, including new media (Hesmondhalgh, 2021; Meier and Manzerolle, 2018; Webster, 2021), psychology (Jambon and Smetana, 2012; Nandedkar and Midha, 2012), marketing (Clement et al., 2012; Datta et al., 2017; Hirschman and Holbrook, 1982; Papies et al., 2011), economics (Peitz and Waelbroeck, 2006), and information systems (IS) (Dewan et al., 2017; Dewan and Ramaprasad, 2014; Gopal et al., 2004; Im et al., 2019). Previous literature in this field has considered the impact of various aspects of new media, such as peer-to-peer music sharing, online streaming services, and the introduction of mobile devices and expansion, on music consumption. These areas have attracted considerable attention from researchers, practitioners, and policymakers.

Most of the previous research was focused on how individual users employ new media to consume music based on a psychological or economic perspective. For example, research based on the psychological perspective examined individual factors, such as motivations and benefits, associated with peer-to-peer music sharing and online music streaming usage (Chen et al., 2008; Jambon and Smetana, 2012; Nandedkar and Midha, 2012; Plowman and Goode, 2009). The economic perspective-based research explored the effect of peer-to-peer music sharing on the dwindling music purchases at an individual level (Bakker, 2005; Bhattacharjee et al., 2003; Gopal et al., 2004). However, the economic impact of peer-to-peer music sharing on music industry sales is disputable (Liebowitz and Watt, 2006; Peitz and Waelbroeck, 2006).

New media and explorative music consumption

In numerous studies, the way in which new media influences specific music consumption behavior, namely, explorative music consumption, has been examined (Datta et al., 2017; Im et al., 2019; Papies et al., 2011; Tepper and Hargittai, 2009). By definition, exploration refers to attempts to adjust a stimulation from the environment by affording access to a new stimulation that was not previously available (Raju, 1980). In the context of listening to music, explorative music consumption is defined as the pursuit of novelty (i.e. embracing unexpected, surprising, new, or unfamiliar things) and variety (i.e. overcoming inconsistencies by seeking to vary behavior) in choosing music (Tepper and Hargittai, 2009).

Explorative music consumption can be helpful for a better appreciation of music and enjoyment (Tepper and Hargittai, 2009). Such enjoyment is derived from stimulating choices and the act of discovering something new that satisfies the preferences of certain individuals. Explorative music consumption also has certain potential benefits for musicians and the industry. For example, the explorative music consumption of various individuals provides more room for new musicians and genres to break through the status quo from the supply side.

The literature indicates that users can explore the music that is new to them by employing two main methods (Tepper and Hargittai, 2009). First, they may rely on their social networks for recommendations (Dewan et al., 2017). Exchanging information with the people in one’s networks is an important avenue for exploring new music. Second, they may discover new music owing to their exposure to different media. For example, users rely on traditional mass media, such as television and radio, as essential sources for discovering new music. In addition, they may find suggestions through digital media as well. New media could promote the discovery of new music and improve the capacity of individuals toward this purpose. With new media, the assortment of music and other cultural products has become more diverse and expansive (Im et al., 2019). New media also offer tools for users to navigate through expanded sets of music by reducing the cost of searching and browsing. These advantages enable users to experiment with new music at a relatively low cost and risk.

Some empirical studies found that advances in new media have prompted the users to discover new music, in addition to leveraging social networks and traditional channels (Tepper and Hargittai, 2009). Specifically, peer-to-peer music sharing is more likely to be employed than browsing by users with higher socioeconomic backgrounds and those who are opinion leaders in explorative music consumption. Relative to music downloading, in which music discovery is expensive, online streaming services lead to a substantial increase in the consumption diversity in the initial months of their adoption (Datta et al., 2017). Users play more diverse music over a longer-term, although the effects diminish over time. Free online streaming services with limited mobility could facilitate more music discovery and stimulate the use of different channels that allow more mobile consumption (Aguiar, 2017).

Previous literature has mainly focused on how new media directly affect different types of music consumption. Even though individual differences, such as psychological factors, may play a role in this relationship, those aspects have not been discussed in the literature. This lack of understanding is surprising, as psychological factors have already been demonstrated to influence music consumption (Tepper and Hargittai, 2009). Thus, little information is available concerning the influence of psychological factors on how individuals choose to access new music with new media.

Our study aims to fill this gap by examining how cross-media usage in the digital era affects explorative music consumption from a psychological lens. We first offer a snapshot of the characteristics of the users, who are in the realm of cross-channel usage (Kim, 2014). More importantly, we endeavor to provide evidence and theoretical interpretations for the association between cross-channel usage, cross-network usage, and music discovery. In addition to taking a close look at the role of new media in music discovery, we also analyze individual factors that moderate the relationship between new media and new music exploration. Specifically, gender differences were found to affect the explorative consumption and new media adoption and usage in different contexts, including media and entertainment contexts (Chen et al., 2017; Gnambs, 2021; Hou and Elliott, 2016; O’Neill, 1997; Punter et al., 2017). Prior studies indicate that the OSL tends to be different for each gender (Zuckerman and Kuhlman, 2000). Thus, we specifically examine how gender moderates the relationship because gender differences may systematically affect exploratory consumer behaviors and new media usage (Steenkamp and Burgess, 2002; Zuckerman and Kuhlman, 2000).

Theoretical framework and hypotheses development

OSL theory and explorative consumption behavior

The OSL theory is one of the central ideas that explain the explorative tendencies of consumers (Lin and Lin, 2012; Menon and Kahn, 1995; Raju, 1980). The OSL indicates the amount of stimulation an individual pursues from all internal and external channels across possible situations and over time. In the early years, psychologists suggested that an individual might explore the environment around him or her to achieve a satisfactory stimulation level (Berlyne, 1960; Harrison, 1968). They all based their arguments on the theory of OSL. On one hand, they argued that when a situation provided low stimulation below an optimal level, an individual would seek increased stimulation. This seeking would lead to explorative behaviors, such as novelty-seeking or variety-seeking, whereby the individual would aim to increase the stimulation from any channel. On the other hand, if the situation generated a higher stimulation level than the optimal level, then the individual would desire a more moderate environment by avoiding the additional stimulation. Based on this theory, researchers suggested that people might engage in activities that would be intrinsically rewarding in the absence of external stimulation, and such behaviors would be motivated by the expectation of experiencing novelty and variety or satisfying curiosity from those activities (Helm and Landschulze, 2009; Menon and Kahn, 1995).

The OSL is a function of relevant personality traits and characteristics (Baumgartner and Steenkamp, 1996; Steenkamp and Burgess, 2002). For example, intolerant persons are expected to have a lower OSL (Berlyne, 1960). Demographic variables, such as gender, income, and education level, are also correlated with the OSL (Kwon and Workman, 1996; Steenkamp and Burgess, 2002). The OSL tends to be higher for men and decreases across the life span (Zuckerman and Kuhlman, 2000).

In summary, an individual’s OSL and its difference from the level of an actual stimulation can be responsible for the level of intrinsic motivation of the individual toward explorative behaviors, depending on the individual’s personality traits and characteristics.

Explorative consumption of digital goods in new media environment

This study focuses on OSL and its relationships with the consumption of digital goods. More specifically, the relationships involving OSL, general exploratory tendencies, and selected personal attributes and characteristics are examined in the digital music consumption context. As noted, the previous research has mainly investigated physical consumable goods (Adomavicius et al., 2015). However, digital goods, such as digital music streaming, have unique characteristics. Digital goods are more easily distributed and consumed than their physical counterparts. Most digital goods can be used multiple times without any degradation. For example, a digital music product, such as a song or a collection of songs, can typically be rearranged multiple times with no loss in quality, thus leading to a higher probability of explorative consumption than in an ordinary consumption context.

The probability of explorative consumption can be elevated by the superior flexibility in consuming music induced by a cross-media environment. Users have started possessing and using multiple devices. The situations in which multiple devices are combined and used together have become more common (Jokela et al., 2015). For example, digital content, such as digital music and video, can be seamlessly streamed or downloaded on multiple devices. Moreover, users can easily switch from one service to another because of the increased accessibility and lowered switching costs in the digital environment (Han et al., 2016). The experience of the users of this flexibility may have a behavioral impact on the choice and consumption not present in the case of physical consumable goods. We address this potential difference in explorative behavior with regard to consuming digital goods. Figure 1 presents the conceptual framework of the study.

Figure 1.

Conceptual framework.

In this study, we explore how cross-media usage is related to explorative behavior in digital music streaming. Based on the OSL perspective, we hypothesize that a high level of cross-media usage is positively associated with higher general explorative tendencies (i.e. higher OSL). We focus on two different cross-media usage practices, namely, cross-channel usage and cross-network usage (Heyer et al., 2008; Huang et al., 2016). We propose that the two cross-media usage practices are proxies for indicating an individual’s OSL. Cross-media usage tends to offer more stimulation (i.e. through using different devices or products). Individuals with higher OSLs are more likely to adopt more stimulating alternatives than those with lower OSLs (Helm and Landschulze, 2009; Raju, 1980).

First, cross-channel usage refers to using multiple devices (e.g. PCs, mobile devices, and tablets) for a single service. Cross-channel usage occurs when a user navigates to another device that connects to the same service. The introduction and expansion of mobile phones and tablets offer the users better Internet access, entertainment content, and support for productivity. In a ubiquitous computing environment, individuals commonly use multiple devices in multiple contexts for multiple purposes (Scheepers and Middleton, 2013). According to the OSL theory, high-OSL individuals have a stronger desire to explore situations or activities which are novel and complex (Raju, 1980). When exposed to the same stimulus, high-OSL individuals are more likely to experience the stimuli than their low-OSL counterparts. Therefore, high-OSL individuals are more likely to employ new channels in a digital consumption context than the existing channels (Gu et al., 2018).

Moreover, motivated by the need for stronger stimulations, the high-OSL individuals are likely to enjoy using cross-channels because the different user interfaces and experiences increase the stimulus input, thus offering individuals higher levels of stimulation (Baumgartner and Steenkamp, 1996). They are likely to perceive a higher level of ease of use regarding using different devices than low-OSL individuals (Gu et al., 2018). Researchers have found that cross-channel usage is related to user innovativeness and increased sales (Farag et al., 2006; Kumar and Venkatesan, 2005). In this regard, multi-device usage in this study may indicate the high-OSL of individuals, and thus, those individuals who use multiple devices are more likely to explore new music. Therefore, we hypothesize the following:

Hypothesis 1: Multi-device usage is positively associated with explorative behavior in digital music consumption.

Second, cross-network usage refers to using multiple products (e.g. mobile applications) for a single purpose. Cross-network usage occurs when a user explores different alternatives for the same purpose. For example, many competing mobile applications provide similar services and features. In addition to multihoming in the literature (Gu et al., 2016; Landsman and Stremersch, 2011), the phenomenon of multiple usages of alternative products becomes convenient in the ubiquitous computing context (Yoo, 2010). The usage of multiple applications for the same purpose can be viewed as the users’ choices according to their peculiar characteristics and situations. The literature suggests that high-OSL individuals have a lower level of tolerance for repeated stimuli and, thus, are more bored in a stable situation (Raju, 1980; Steenkamp and Burgess, 2002). Consequently, high-OSL individuals are more open to new experiences and receptive to engaging in novel situations than their counterparts (Helm and Landschulze, 2009). OSL is an influential personal attribute in cross-network usage because it relates to variety-seeking in the users’ adoption and usage of multiple services for the same purpose (Gu et al., 2016). Based on this argument, multi-app usage in this study may be linked to the high-OSL of individuals, and thus, those who use multiple mobile applications are more likely to explore new music. Consequently, we hypothesize the following:

Hypothesis 2: Multi-app usage is positively associated with explorative behavior in digital music consumption.

Moderating effects of gender on relationships between cross-media usage and explorative consumption

Next, we propose that the association between cross-media usage and explorative consumption behavior of digital music is influenced by relevant exogenous variables, such as individual attributes. Demographic characteristics such as age, income, gender, marital status, and education are examples of variables to be considered when explaining explorative tendencies (Steenkamp and Burgess, 2002). Research on the consumption of music and other forms of entertainment reveals relationships involving demographic factors (Chamorro-Premuzic et al., 2010). In our study, we examine the role of gender.²

Among others, gender differences may particularly affect the attitudes toward music and technology adoption and usage of digital music channels in different aspects. First, gender differences exist in new media technology adoption and usage. Earlier studies show that women tend to view men as more capable of understanding new technology (Wasserman and Richmond-Abbott, 2005). Furthermore, gender differences in technology adoption and usage has been reported in the literature (Hakkarainen et al., 2000; Kuhlemeier and Hemker, 2007; North et al., 2000). Studies have also indicated that men are more positive toward and confident in using new media technology (Colley, 2008; North et al., 2000; O’Neill, 1997). In contrast to the previous findings, more recent studies revealed a less consistent pattern, showing that these differences might have reduced or even found women outperformed men in new media technology literacy (Gnambs, 2021; Punter et al., 2017).

Interestingly, another stream of literature examines gender differences in media and entertainment consumption (Jansz et al., 2010). Entertainment content can lead to a more positive attitude for women than for men (Sun et al., 2010). Relatedly, women are more likely to possess an intrinsic reinforcement of entertainment (Kuss and Griffiths, 2011) and are more sensitive to the motivation of enjoyment in consuming media (Chen et al., 2017). Women also care more about their emotions when using new media (Zhang et al., 2009). Finally, women and men have different tendencies in novelty seeking and variety seeking. Studies show that women are more likely than men to be variety seekers and tend to develop higher purchase intentions when exposed to unfamiliar brands (Hou and Elliott, 2016; Karpinska-Krakowiak, 2021). In summary, this literature indicates that being a woman is associated with explorative consumption in the entertainment context. Based on these findings, we propose that the relationship between cross-media usage and explorative music consumption is stronger for women than for men in our research context. Thus, we hypothesize as follows:

Hypothesis 3a: The association between multi-device usage and explorative music consumption is stronger for women than for men.

Hypothesis 3b: The association between multi-app usage and explorative music consumption is stronger for women than for men.

The research model of the study is presented in Figure 2.

Figure 2.

Research model.

Empirical approach

We designed a detailed survey questionnaire on digital music consumption behavior in collaboration with a leading music-streaming service provider in China. The service was launched in 2013 and acts as a comprehensive music platform, providing basic services, such as streaming and downloading music, and personalized services, such as radio stations and algorithmic recommendations.

We designed the pilot survey and collected initial feedback from industry experts and researchers in relevant domains based on the objective of this research. We also performed a preliminary analysis and amended our survey questions. The final survey questionnaire was an outcome of two rounds of pilot surveys and iterations. We designed three categories of questions on music consumption behavior: listening, exploration, and payment. In addition, we collected the basic demographic information of the respondents. We recruited our survey subjects from a number of major universities in China. All subjects were undergraduates or graduates in those universities. The service provider confirmed that these subjects were the most active users of music streaming platforms and contributed the most revenue. We conducted the online surveys in two rounds: the first round in fall 2017 and the second in early 2018.

In total, we collected 1140 responses in our survey. We eliminated 24 incomplete surveys (e.g. missing values), thus leaving a final sample of 1116 respondents. Our sample included more women (747, 66.9%) than men (369, 33.1%).³ Most were in their undergraduate studies (856; 76.7%); the remainder were in their graduate studies (260; 23.3%). The service provider confirmed that the gender and age ratios adequately represented those of all their registered customers, thereby diminishing the potential for a non-response bias.

We captured the data related to explorative music consumption by directly asking the respondents how many new songs they found and fully listened to per month on average. The choices were divided into five interval scales: “none,” “1–5 new songs,” “6–20 new songs,” “21–50 new songs,” and “more than 50 new songs.”

The respondents’ cross-media usage, which was our focal variable of interest, was collected from their responses to the questions on how many devices (multi-device usage) and how many music mobile applications (multi-app usage) they used. We operationalized the multi-device usage to be “one” if the respondents indicated that they used more than one device, and to be “0” otherwise (i.e. the use of a single device). Similarly, we operationalized the multi-app usage to be “one” if the respondents indicated that they used more than one mobile application, and to be “0” otherwise (i.e. they used a single application).

Our analysis included several explanatory variables. First, we examined the overall mobile app usage pattern of each respondent, including their listening intensity (how many songs do you listen to per week?), app usage frequency (how often do you use a mobile app[s]?), and whether the respondent paid for the services (i.e. app payment). Second, we constructed a set of variables capturing the payment behavior of each user in relation to other music-related consumption, such as live concerts (i.e. how many times do you attend a live concert per year?) and musician goods (i.e. how much money do you spend on your favorite musical artist?). Third, we included information related to user demographics, including gender and age. Finally, we included the overall music consumption tendencies of each respondent, such as their musical preferences in the source (by singer, playlist, genre, random, and song name) and genre (Pop, Rock, Hip-hop, Rap, Jazz, Classic, and others). Table 1 shows the operational definitions of the variables. Table 2 summarizes the descriptive statistics and variable correlations.

Table 1.

Operational definition of variables.

Type	Variable	Operationalization
Dependent variable	Explorative music consumption	How many new songs do you explore per month? (interval scale: 1–5)
Focal independent variables	Multi-device usage	One if using more than one type of device or 0 otherwise
Focal independent variables	Multi-app usage	One if using more than one application or 0 otherwise
App usage pattern	Listening intensity	How many songs do you listen to per week? (interval scale: 1–5)
	App usage frequency	How often do you use the mobile app(s)? (interval scale: 1–5)
	App payment	One if paying for apps or 0 if using the apps free
Other music-related consumption	Live concert	How many times do you attend live concerts per year? (interval scale: 1–5)
Other music-related consumption	Musician goods	How much money do you spend on your favorite musician? (interval scale: 1–5)
Demographic	Gender^a	One if man, 0 if woman
Demographic	Grade	Undergraduate 1–4, graduate 1–3, doctoral student
Musical preference	Music source	By singer, by playlist, by genre, by random, by song name
Musical preference	Music genre	Pop, rock, hip-hop, rap, jazz, classic, others

Our surveys provided the “other” option in the gender question but there was no other response than the “man” and “woman” options.

Table 2.

Descriptive statistics and correlation of variables.

Variable	Mean	SD	1	2	3	4	5	6	7	8	9	10
1. Explorative music consumption	2.32	0.96	1
2. Multi-device usage	0.84	0.37	.1474*	1
3. Multi-app usage	0.61	0.49	.1194*	.0767	1
4. Listening intensity	2.79	0.77	.3744*	.1144*	.1887*	1
5. App usage frequency	4.12	0.88	.2985*	.0523	.1913*	.5453*	1
6. App payment	0.47	0.50	.2082*	.0942*	.2172*	.2199*	.2179*	1
7. Live concert	1.47	0.87	.2122*	.0174	.1072*	.0896*	.0953*	.1380*	1
8. Musician goods	1.78	1.20	.1649*	.0191	.1032*	.1291*	.1062*	.3704*	.3922*	1
9. Gender	0.33	0.47	−.0744	.1033*	−.0254	.0398	.0391	.0665	−.0013	−.0474	1
10. Grade	2.71	1.91	−.0269	−.0721	−.0376	−.1308*	−.0517	−.0274	.0714	.0119	−.0488	1

The symbol * refers to significance at the 5% level.

Results

Descriptive analysis

As our analysis is based on responses to a survey, we first present the survey’s descriptive findings before the main analysis. The result in Section A of Table 3 shows that the users did not find new songs often for explorative music consumption. Of the respondents, 43.55% discovered less than five songs per month, followed by 27.24% of the respondents, exploring 6–20 new songs per month. Overall, the users did not appear to be seriously motivated to actively search for new songs.

Table 3.

Descriptive analysis of overall music and music-related consumption.

Section	Item	Frequency	Percent
A. Explorative music consumption (per month)	None	211	18.91
	1–5 new songs	486	43.55
	6–20 new songs	304	27.24
	21–50 new songs	83	7.44
	More than 50	32	2.87
	Total	1116	100
B. Multi-device usage	One device	178	15.95
	Two devices	593	53.14
	More than 2	345	30.91
	Total	1116	100
C. Multi-app usage	One app	431	38.62
	Two apps	483	43.28
	Three apps	149	13.35
	More than 3	53	4.75
	Total	1116	100
D. Listening intensity (per day)	Seldom	12	1.08
	1–5 songs	438	39.25
	6–20 songs	443	39.70
	More than 20	223	19.98
	Total	1116	100
E. App usage frequency	Few times per year	2	0.18
	Few times per month	24	2.15
	A few times per week	288	25.81
	Less than 1 hour per day	330	29.57
	More than 1 hour per day	472	42.29
	Total	1116	100
F. App payment	No	588	52.69
	Yes	528	47.31
	Total	1116	100
G. Live concerts (per year)	Not at all	778	69.71
	One concert	211	18.91
	Two concerts	89	7.97
	Three concerts	22	1.97
	More than 3	16	1.44
	Total	1116	100
H. Musician goods (per year)	Not at all	736	65.95
	1–10 Yuan	77	6.90
	11–100 Yuan	140	12.54
	101–1000 Yuan	136	12.19
	More than 1000 Yuan	27	2.42
	Total	1116	100

While mobile devices were the dominant device for digital music consumption, many used multiple devices for music consumption. The majority used both PCs and mobile phones at the same time (53.14%). Of the users, 30.91% used more than two devices, which included other devices, such as tablets (Section B, Table 3). Furthermore, more than 61% of the users regularly used more than one music streaming application. Only 38.62% of the users used a single application (Section C, Table 3). The results suggest that the users did not necessarily adhere to one music streaming channel for their music consumption. Most music streaming platforms/services are free for use, and each platform offers unique content from different artists, and thus, utilizing multiple apps and switching between them are common for users. Quickly switching between different music apps would be possible if the content could be migrated and changes in access occurred. We found that a majority of the users in our sample listened to 1–20 songs per day, on an average (78.95%), whereas a small portion of them listened to more than 20 songs per day (19.98%) (Section D, Table 3).

Most users used the mobile app(s) daily, with 42.29% spending more than 1 hour and 29.57% spending less than 1 hour per day (Section E, Table 3). Of the users, 47.31% were paying for using the mobile apps (Section F, Table 3). Live concerts were another primary source of revenue in the music industry. Less than 31% of the respondents in our survey paid for one or more live concerts in the past year (Section G, Table 3). Only 34% of the survey respondents paid for their favorite musicians in any form, including CDs and merchandising goods (Section H, Table 3).

Effect of cross-media usage on explorative music consumption

For the main analysis, we ran separate ordinary least squares (OLS) regressions. To examine the relationship between cross-media usage and explorative behavior, we performed two OLS regressions with explorative music consumption as the outcome variable and multi-device usage and multi-app usage as variables of interest. We found that multi-device usage was positively associated with explorative music consumption. Column 1 of Table 4 shows that using multiple devices for music listening significantly influenced the explorative music consumptions of the users. The results supported Hypothesis 1, which posits that the use of multiple devices, a proxy for higher general explorative tendencies, leads to more explorative behavior in the cross-media environment.

Table 4.

Effects of multi-device usage and multi-app usage on explorative music consumption

DV: Explorative music consumption (OLS)
Variables	(1)	(2)
Variables	Coefficient (SE)	Coefficient (SE)
Multi-device usage	0.227*** (0.071)
Multi-app usage		−0.034(0.052)
Listening intensity	0.269*** (0.042)	0.278*** (0.042)
App usage frequency	0.117*** (0.032)	0.116*** (0.032)
App payment	0.145*** (0.054)	0.161*** (0.054)
Live concert	0.138*** (0.034)	0.141*** (0.035)
Musician goods	0.007(0.024)	0.006(0.024)
Gender	−0.159*** (0.054)	−0.140*** (0.053)
Grade	0.01(0.013)	0.008(0.013)
Listening by singer	−0.091* (0.051)	−0.096* (0.051)
Listening by playlist	0.102* (0.056)	0.121** (0.056)
Listening by genre	0.126** (0.062)	0.137** (0.062)
Listening by random	0.279*** (0.054)	0.287*** (0.054)
Listening by song	0.010(0.056)	0.018(0.056)
Genre_Pop	0.067(0.086)	0.081(0.087)
Genre_Rock	0.183*** (0.062)	0.184*** (0.062)
Genre_Hip-hop	0.311*** (0.087)	0.315*** (0.087)
Genre_Rap	0.073(0.077)	0.082(0.077)
Genre_Jazz	0.215*** (0.076)	0.214*** (0.076)
Genre_Classic	0.017(0.051)	0.024(0.052)
Genre_Others	0.143** (0.056)	0.143** (0.057)
Constant	0.224(0.154)	0.365** (0.152)
R ²	0.286	0.279
Adjusted R²	0.273	0.266
N	1116	1116

This table reports the results of the OLS regressions. Robust standard errors are in parentheses. DV: dependent variable.

The symbols *, **, and *** refer to significance at the 10%, 5%, and 1% levels, respectively.

However, we did not find a significant relationship between multi-app usage and explorative music consumption (Column 2 of Table 4). Thus, Hypothesis 2 was not supported. The results indicate that the use of multiple music applications might not be mainly driven by an individual’s general explorative tendencies (or intrinsic motivation) but could be because of external incentives. In the Chinese market, a few major players have competed with each other by providing exclusive content and features. In this regard, in the Chinese context of our study, the multi-app usage could be driven more by a complementarity from using different mobile apps than by intrinsic motivation for explorative behavior (Gu et al., 2016). Because of this competitive landscape, in some cases, users use multiple apps to listen to their favorite (or preferred) music and not for exploring new music. In this situation, multi-app usage is likely to be driven by external benefits and not by general explorative tendencies.

In addition to the effects of the focal variables related to OSL, the results show that listening time, app usage frequency, and payment for live concerts were positively associated with explorative music consumption. In addition, female participants exhibited more explorative behavior in digital music consumption than their male counterparts did. In terms of the musical preferences of the users, listening to music based on the singer was negatively associated with explorative music consumption, whereas listening based on “playlist” and “genre,” as well as at “random,” was positively related to the exploration of more new music. The findings showed that listening based on “playlist” and “genre” might promote explorative music consumption based on their specific preferences, whereas listening at “random” may offer personalized random lists based on the algorithmic recommendations. In terms of the preferred genre, the users who preferred to listen to Rock, Hip-hop, or Jazz music genres were more likely to explore new music. The findings indicated that genre preferences might be formed because of certain personal traits related to explorative behavior. Thus, the results provide us with interesting insights that could help streaming music services and platforms.

Effect of interaction between cross-media usage and gender on explorative music consumption

Next, we examined how the cross-media usage of the users interacted with their characteristics in influencing explorative music consumption. Specifically, we examined how gender (Hypotheses 3a, b) influenced the relationship between cross-media usage and explorative music consumption. We conducted another set of OLS regressions with explorative music consumption as the outcome variable and the interaction terms as variables of interest.

The results show that the effects of multi-device usage were not moderated by the gender of the respondents to a statistically significant degree (Column 1 of Table 5, Hypothesis 3a). However, we identified that gender significantly moderated the effects of multi-app usage on explorative music consumption as hypothesized. The results show that this relationship (Column 2 of Table 5, Hypothesis 3b) was negatively influenced in the case of men, suggesting that women’s multi-app usage was more likely to be related to explorative music consumption. It also offers a clue to interpreting the insignificant relationship between multi-app usage and explorative music consumption. From another perspective, our analysis consistently identified that being a woman was associated with more new music exploration, and this tendency was strengthened by the use of multiple applications. Thus, Hypothesis 3a was not supported, whereas Hypothesis 3b was supported, suggesting that the association between multi-app usage and explorative music consumption is stronger for women than for men.

Table 5.

Interaction between cross-media usage and demographic factor in influencing explorative music consumption.

DV: Explorative music consumption (OLS)
Variables	(1)	(2)
Variables	Coefficient (SE)	Coefficient (SE)
Multi-device usage	0.218*** (0.079)
Multi-app usage		0.060(0.064)
Multi-device usage × gender	0.038 (0.171)
Multi-app usage × gender		−0.273** (0.106)
Listening intensity	0.269*** (0.042)	0.275*** (0.042)
App usage frequency	0.117*** (0.032)	0.115*** (0.032)
App payment	0.145*** (0.054)	0.165*** (0.054)
Live concert	0.139*** (0.034)	0.141*** (0.035)
Musician goods	0.007(0.024)	0.005(0.024)
Gender	−0.192(0.163)	0.024(0.085)
Grade	0.010(0.013)	0.008(0.013)
Music source	Yes	Yes
Music genre	Yes	Yes
Constant	0.231(0.158)	0.314** (0.153)
R ²	0.286	0.283
Adjusted R²	0.272	0.270
N	1116	1116

This table reports the results of the OLS regressions. Robust standard errors are in parentheses. DV: dependent variable.

The symbols ** and *** refer to significance at the 5% and 1% levels, respectively.

The results of the hypotheses tests are summarized in Figure 3.

Figure 3.

Summary of the hypotheses test.

Discussion and conclusion

Summary of findings

In this study, we focused on explorative music consumption in the context of mobile music streaming. Based on the theoretical perspective of the OSL, we revealed how different characteristics of cross-media usage lead to explorative music consumption. Based on the survey responses of 1116 college students in China, we demonstrated that multi-device usage is positively related to explorative music consumption. However, we could not find a significant relationship between multi-app usage and explorative music consumption. On one hand, the findings support the idea that multi-device usage is a potentially appropriate indicator of a higher OSL. In addition, multi-device usage is relevant to different situations that promote explorative consumption behavior, such as different locations, times, or user interfaces, shaping the conditions for exploration. On the other hand, multi-app usage does not correlate with explorative music consumption in our analysis. The results may be related to the Chinese context, given that multi-app usage is a considerably common practice in China owing to the use of free services, lowering the barrier for new trial users. Moreover, different music-streaming platforms provide unique content and features, driving the extrinsic motivations of users to utilize multiple services rather than an intrinsic motivation to explore new music. The findings from the interaction analyses show that the gender of the users influences the relationship between multi-app usage and explorative music consumption, supporting the idea that gender differences may systematically influence exploratory consumer behavior and new media usage in the context of music consumption (Steenkamp and Burgess, 2002; Zuckerman and Kuhlman, 2000). Our results show the positive interaction effects of being a woman and multi-app usage on new music exploration.

Academic implications

Our study makes contributions to the new media literature and practices in the area. Most of all, this study extends our knowledge of how new media affect music consumption behaviors. While the existing literature has mainly focused on the direct effects of new media, we have analyzed the individual factors that moderate the relationship between new media and the exploration of new music. Our study has focused on individual differences, such as psychological factors that might play a role in the relationship (Webster, 2021). We have provided evidence and theoretical interpretations that explain the associations among cross-channel usage, cross-network usage, and music discovery from a psychological perspective. These findings expand our understanding of how these psychological factors influence music consumption (Tepper and Hargittai, 2009).

Relatedly, we contribute to the literature on exploratory user behavior by linking the OSL theory to the digital music consumption context (Adomavicius et al., 2015). Although the OSL theory has been extensively examined in diverse literature (Raju, 1980; Steenkamp and Burgess, 2002), it has not been rigorously investigated to address the role of OSL in new media literature. Our findings support the generalizability of the OSL theory in the new media context by examining the role of cross-media usage as a proxy for general explorative tendencies. Our findings also suggest that the drivers of explorative behaviors are stable, irrespective of different settings (Helm and Landschulze, 2009).

Practical implications

The study has practical implications. Specifically, the findings of our study provide actionable insights for online music platform operators in China and other countries. The operators can leverage user information on cross-media usage to understand their OSLs and accordingly offer different music-related services. Because cross-media usage may indicate a higher OSL, the platform operators can target those users to promote more novel music content. For example, multi-device users can be more appropriate target customers for music-exploration-related promotions. Moreover, integrating the information into their demographics and preferences may enhance the effectiveness of the communication. Understanding the gender differences in explorative consumption behavior should provide practitioners with relevant insight.

Future research

Our findings will open new avenues for future research. We relied on survey responses to examine the relationships between cross-media usage and explorative music consumption. Our study design prevented us from clearly identifying the effects of cross-media usage and the possible mechanisms of the effects. Thus, a different methodological approach can be selected in future research to address the weaknesses of the survey data.

Furthermore, the sampling procedure can be improved. We analyzed data from a single platform in China. Although this platform is one of the largest in the nation, and China is the second-largest digital music market, it raises external validity concerns. How the direction and magnitude of the effects will differ across platforms and countries can be evaluated in future studies. Furthermore, gender was a key variable in our research context, but we have not examined research subjects outside of the traditional gender binary. Given the importance of the non-binary gender identity in new media research (Bond and Miller, 2021; Dhaenens and Burgess, 2018), it will be a promising future research direction to examine the relationship between the non-binary gender identity, new media technology, and explorative music consumption. For example, future research may examine how the non-binary gender identity influences the relationship between new media technology usage and explorative music consumption.

Finally, most music-streaming service providers offer different features to users, such as algorithm-based recommendations, to help them explore new music. Although the features should be related to the listening patterns of users, we did not survey the actual usage of specific features or the users’ attitudes toward them. Differences may exist across users in leveraging the features used to explore new music, leading to different patterns of explorative music consumption. Therefore, how different uses of the features are related to the explorative music consumption patterns of users can be examined in future research.

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Sunghan Ryu

Notes

Author biographies

Sunghan Ryu is an associate professor at USC-SJTU Institute of Cultural and Creative Industry, Shanghai Jiao Tong University. He is also the founding director of the Center for Digital Creative Enterprise Research and serves as the series editor for the Digital Entertainment series of SAGE Business Cases. His research and teaching interests include digital innovations in cultural and creative domains and effective digital technology applications in the creative entrepreneurial context.

Shantanu Dutta is the Dave and Jeanne Tappan Chair in Marketing and Professor of Marketing in the Marshall School of Business, University of Southern California. He is an expert on strategic marketing, especially in high technology markets. He also studies how firms use distribution, partnerships, and value pricing to build competitive advantage.

Baizhu Chen is a Professor of Clinical Finance and Business Economics in Marshall School of Business, University of Southern California. He studies macroeconomics and international economics, with an emphasis on China.

References

Adomavicius

Bockstedt

Curley

(2015) Bundling effects on variety seeking for digital information goods. Journal of Management Information Systems 31(4): 182–212.

Aguiar

(2017) Let the music play? Free streaming and its effects on digital music consumption. Information Economics and Policy 41: 1–14.

Bakker

(2005) File—sharing—fight, ignore or compete: paid download services vs. P2P-networks. Telematics and Informatics 22(1–2): 41–55.

Baumgartner

Steenkamp

J-BEM

(1996) Exploratory consumer buying behavior: conceptualization and measurement. International Journal of Research in Marketing 13(2): 121–137.

Berlyne

(1960) Conflict, Arousal, and Curiosity. New York: McGraw Hill.

Bhattacharjee

Gopal

Sanders

(2003) Digital music and online sharing: software piracy 2.0? Communications of the ACM 46(7): 107–111.

Bond

Miller

(2021) YouTube as my space: the relationships between YouTube, social connectedness, and (collective) self-esteem among LGBTQ individuals. New Media & Society. Epub ahead of print 7 December. DOI: 10.1177/14614448211061830.

Campbell

(2007) A cross-cultural comparison of perceptions and uses of mobile telephony. New Media & Society 9(2): 343–363.

Chamorro-Premuzic

Swami

Cermakova

(2010) Individual differences in music consumption are predicted by uses of music and age rather than emotional intelligence, neuroticism, extraversion or openness. Psychology of Music 40(3): 285–300.

10.

Chen

Zhang

KZK

Gong

, et al. (2017) Examining the effects of motives and gender differences on smartphone addiction. Computers in Human Behavior 75: 891–902.

11.

Chen

Y-C

Shang

R-A

Lin

A-K

(2008) The intention to download music files in a P2P environment: consumption value, fashion, and ethical decision perspectives. Electronic Commerce Research and Applications 7(4): 411–422.

12.

Clement

Rangaswamy

Vadali

(2012) Consumer responses to legal music download services that compete with illegal alternatives. Service Science 4(1): 4–23.

13.

Colley

(2008) Young people’s musical taste: relationship with gender and gender-related traits. Journal of Applied Social Psychology 38(8): 2039–2055.

14.

Danckwerts

Kenning

(2019) “It’s MY service, it’s MY music”: the role of psychological ownership in music streaming consumption. Psychology & Marketing 36(9): 803–816.

15.

Datta

Knox

Bronnenberg

(2017) Changing their tune: how consumers’ adoption of online streaming affects music consumption and discovery. Marketing Science 37(1): 5–21.

16.

Dewan

Ramaprasad

(2014) Social media, traditional media, and music sales. MIS Quarterly 38(1): 101–122.

17.

Dewan

Y-J

Ramaprasad

(2017) Popularity or proximity: characterizing the nature of social influence in an online music community. Information Systems Research 28(1): 117–136.

18.

Dhaenens

Burgess

(2018) “Press play for pride”: the cultural logics of LGBTQ-themed playlists on Spotify. New Media & Society 21(6): 1192–1211.

19.

Farag

Weltevreden

Van Rietbergen

, et al. (2006) E-shopping in the Netherlands: does geography matter? Environment and Planning B: Planning and Design 33(1): 59–74.

20.

Glitsos

(2019) The “creative listener”: Internet, music, and the computer-bodymind somatechnic. In: Glitsos

(ed.) Somatechnics and Popular Music in Digital Contexts. Cham: Springer, pp. 99–122.

21.

Gnambs

(2021) The development of gender differences in information and communication technology (ICT) literacy in middle adolescence. Computers in Human Behavior 114: 106533.

22.

Gopal

Sanders

Bhattacharjee

, et al. (2004) A behavioral model of digital music piracy. Journal of Organizational Computing and Electronic Commerce 14(2): 89–105.

23.

Jiang

L-B

, et al. (2018) Exploring the impact of optimum stimulation level on individual perceptions of IT innovations: a trait hierarchical model perspective. SIGMIS Database 49(1): 67–92.

24.

L-B

Wang

(2016) Multi-homing on SNSs: the role of optimum stimulation level and perceived complementarity in need gratification. Information & Management 53(6): 752–766.

25.

Hakkarainen

Ilomäki

Lipponen

, et al. (2000) Students’ skills and practices of using ICT: results of a national assessment in Finland. Computers & Education 34(2): 103–117.

26.

Han

Park

(2016) Mobile app analytics: a multiple discrete-continuous choice framework. MIS Quarterly 40(4): 983–1008.

27.

Harrison

(1968) Response competition, frequency, exploratory behavior, and liking. Journal of Personality and Social Psychology 9(4): 363–368.

28.

Helm

Landschulze

(2009) Optimal stimulation level theory, exploratory consumer behaviour and product adoption: an analysis of underlying structures across product categories. Review of Managerial Science 3(1): 41–73.

29.

Hesmondhalgh

(2021) Is music streaming bad for musicians? Problems of evidence and argument. New Media & Society 23: 3593–3615.

30.

Heyer

Brereton

Viller

(2008) Cross-channel mobile social software: an empirical study. In: Proceedings of the SIGCHI conference on human factors in computing systems, Florence, 5–10 April, pp. 1525–1534. New York: Association for Computing Machinery.

31.

Hirschman

(1980) Innovativeness, novelty seeking, and consumer creativity. Journal of Consumer Research 7(3): 283–295.

32.

Hirschman

Holbrook

(1982) Hedonic consumption: emerging concepts, methods and propositions. Journal of Marketing 46(3): 92–101.

33.

Hou

Elliott

(2016) Gender differences in online auctions. Electronic Commerce Research and Applications 17: 123–133.

34.

Huang

(2016) An empirical study of the cross-channel effects between web and mobile shopping channels. Information & Management 53(2): 265–278.

35.

Song

Jung

(2019) The effect of streaming services on the concentration of digital music consumption. Information Technology & People 33(1): 160–179.

36.

Jambon

Smetana

(2012) College students’ moral evaluations of illegal music downloading. Journal of Applied Developmental Psychology 33(1): 31–39.

37.

Jansz

Avis

Vosmeer

(2010) Playing The Sims 2: an exploration of gender differences in players’ motivations and patterns of play. New Media & Society 12(2): 235–251.

38.

Jayarajah

Kauffman

Misra

(2014) Exploring variety seeking behavior in mobile users. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing: Adjunct Publication, Seattle, WA, 13–17 September, pp. 385–390. New York: Association for Computing Machinery.

39.

Jokela

Ojala

Olsson

(2015) A diary study on combining multiple information devices in everyday activities and tasks. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, Seoul, Republic of Korea, 18–23 April, pp. 3903–3912. New York: Association for Computing Machinery.

40.

Kahn

(1995) Consumer variety-seeking among goods and services: an integrative review. Journal of Retailing and Consumer Services 2(3): 139–148.

41.

Karpinska-Krakowiak

(2021) Women are more likely to buy unknown brands than men: the effects of gender and known versus unknown brands on purchase intentions. Journal of Retailing and Consumer Services 58: 102273.

42.

Kim

(2014) A repertoire approach to cross-platform media use behavior. New Media & Society 18(3): 353–372.

43.

Koo

D-M

S-H

(2010) The interactional effects of atmospherics and perceptual curiosity on emotions and online shopping intention. Computers in Human Behavior 26(3): 377–388.

44.

Kuhlemeier

Hemker

(2007) The impact of computer use at home on students’ Internet skills. Computers & Education 49(2): 460–480.

45.

Kumar

Venkatesan

(2005) Who are the multichannel shoppers and how do they perform? Correlates of multichannel shopping behavior. Journal of Interactive Marketing 19(2): 44–62.

46.

Kuss

Griffiths

(2011) Online social networking and addiction—a review of the psychological literature. International Journal of Environmental Research and Public Health 8(9): 3528–3552.

47.

Kwon

Y-H

Workman

(1996) Relationship of optimum stimulation level to fashion behavior. Clothing and Textiles Research Journal 14(4): 249–256.

48.

Lam

Tan

(2001) The Internet is changing the music industry. Communications of the ACM 44(8): 62–62.

49.

Landsman

Stremersch

(2011) Multihoming in two-sided markets: an empirical inquiry in the video game console industry. Journal of Marketing 75(6): 39–54.

50.

Kirkup

(2007) Gender and cultural differences in Internet use: a study of China and the UK. Computers & Education 48(2): 301–317.

51.

Liebowitz

Watt

(2006) How to best ensure remuneration for creators in the market for music? Copyright and its alternatives. Journal of Economic Surveys 20(4): 513–545.

52.

Lin

C-H

Lin

H-C

(2012) Effects of mood states on variety seeking: the moderating roles of personality. Psychology & Marketing 29(3): 157–166.

53.

Mead

Hardesty

Scott

(2019) Low detail future variety: providing affective relief during repetitive music consumption. Psychology & Marketing 36(11): 975–988.

54.

Meier

Manzerolle

(2018) Rising tides? Data capture, platform accumulation, and new monopolies in the digital music economy. New Media & Society 21(3): 543–561.

55.

Menon

Kahn

(1995) The impact of context on variety seeking in product choices. Journal of Consumer Research 22(3): 285–295.

56.

Nandedkar

Midha

(2012) It won’t happen to me: an assessment of optimism bias in music piracy. Computers in Human Behavior 28(1): 41–48.

57.

Nielsen (2020) Nielsen music 360 China report. Available at: https://www.nielsen.com/wp-content/uploads/sites/3/2019/04/Music2036020China20Highlights.pdf

58.

North

Hargreaves

O’Neill

(2000) The importance of music to adolescents. British Journal of Educational Psychology 70(2): 255–272.

59.

O’Neill

(1997) Gender and music. In: Hargreaves

North

(eds) The Social Psychology of Music. New York: Oxford University Press, pp. 46–63.

60.

Papies

Eggers

Wlömert

(2011) Music for free? How free ad-funded downloads affect consumer choice. Journal of the Academy of Marketing Science 39(5): 777–794.

61.

Peitz

Waelbroeck

(2006) Piracy of digital products: a critical review of the theoretical literature. Information Economics and Policy 18(4): 449–476.

62.

Plowman

Goode

(2009) Factors affecting the intention to download music: quality perceptions and downloading intensity. Journal of Computer Information Systems 49(4): 84–97.

63.

Punter

Meelissen

Glas

(2017) Gender differences in computer and information literacy: an exploration of the performances of girls and boys in ICILS 2013. European Educational Research Journal 16(6): 762–780.

64.

Raju

(1980) Optimum stimulation level: its relationship to personality, demographics, and exploratory behavior. Journal of Consumer Research 7(3): 272–282.

65.

Roehm

Jr Roehm

(2005) Revisiting the effect of positive mood on variety seeking. Journal of Consumer Research 32(2): 330–336.

66.

Ryu

Park

(2018) Do I want to pay to download movies: factors affecting acceptance of legal movie download services. Journal of Information, Communication and Ethics in Society 16(1): 45–60.

67.

Scheepers

Middleton

(2013) Personal ICT ensembles and ubiquitous information systems environments: key issues and research implications. Communications of the Association for Information Systems 33(1): 381–392.

68.

Steenkamp

J-BEM

Burgess

(2002) Optimum stimulation level and exploratory consumer behavior in an emerging consumer market. International Journal of Research in Marketing 19(2): 131–150.

69.

Sun

Lim

Jiang

, et al. (2010) Do males and females think in the same way? An empirical investigation on the gender differences in Web advertising evaluation. Computers in Human Behavior 26(6): 1614–1624.

70.

Tepper

Hargittai

(2009) Pathways to music exploration in a digital age. Poetics 37(3): 227–249.

71.

Warr

Goode

MMH

(2011) Is the music industry stuck between rock and a hard place? The role of the Internet and three possible scenarios. Journal of Retailing and Consumer Services 18(2): 126–131.

72.

Wasserman

Richmond-Abbott

(2005) Gender and the Internet: causes of variation in access, level, and scope of use. Social Science Quarterly 86(1): 252–270.

73.

Webster

(2021) The promise of personalisation: exploring how music streaming platforms are shaping the performance of class identities and distinction. New Media & Society. Epub ahead of print 7 July. DOI: 10.1177/14614448211027863.

74.

Westlund

(2010) New (s) functions for the mobile: a cross-cultural study. New Media & Society 12(1): 91–108.

75.

Yoo

(2010) Computing in everyday life: a call for research on experiential computing. MIS Quarterly 34(2): 213–231.

76.

Yoo

Henfridsson

Lyytinen

(2010) The new organizing logic of digital innovation: an agenda for information systems research. Information Systems Research 21(4): 724–735.

77.

Zhang

Lee

Cheung

, et al. (2009) Understanding the role of gender in bloggers’ switching behavior. Decision Support Systems 47(4): 540–546.

78.

Zuckerman

Kuhlman

(2000) Personality and risk-taking: common bisocial factors. Journal of Personality 68(6): 999–1029.