Factors Affecting Consumers’ Intention to Use Online Music ...1471131/...Korea subscribe to an online music service which is the largest proportion globally [14]. In Korea, domestic

IN DEGREE PROJECT INFORMATION AND COMMUNICATION TECHNOLOGY,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2020

Factors Affecting Consumers’ Intention to Use Online Music Service and Customer Satisfaction in South Korea

MINKI PARK

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

Abstract This study aims to investigate factors that affect consumers’ behavioral intentions to use online music services and the extent to which users of these services are satisfied with their experience. It also seeks to clarify the relationship between customer satisfaction and repurchase intention. The research framework of this thesis is built from the extended united theory of acceptance and use of technology (UTAUT2), and the data from an online survey in South Korea was analyzed using SPSS. The study suggests that the most important factors among users of these services are usefulness, hedonic pleasure, and price value. Customers will be satisfied with the service if they believe that it provides useful functions and amusement, and satisfied customers are likely to purchase the service again. It should be possible to efficiently use the results of this research to establish consumer-centric and efficient marketing strategies within aspects of the online music business and to better understand the behavior of consumers when using these services.

Sammanfattning

Denna studies syfte är att undersöka de faktorer som påverkar konsumenters beteende och avsikter till att använda musiktjänster online, samt i vilken utsträckning användarna av dessa tjänster är nöjda. Studien syftar även till att klargöra förhållandet mellan kundnöjdhet och intentionen att göra ett återköp. Det ramverk som denna uppsats är byggd på är UTAUT2 – “The extended united theory of acceptance and use of technology” och datan från en onlineundersökning i Sydkorea analyserades med hjälp av SPSS. Studien kommer fram till att de viktigaste faktorerna bland användare av dessa tjänster är användbarhet, hedoniskt nöje och prisvärde. Kunder kommer att vara nöjda med tjänsten om de tror att den ger användbara funktioner och underhållning, nöjda kunder kommer även sannolikt att köpa tjänsten igen. Det bör vara möjligt att på ett effektivt sätt använda resultaten av denna forskning för att etablera konsumentcentriska och effektiva marknadsföringsstrategier inom aspekter av onlinemusik-branschen och för att bättre förstå beteendet hos konsumenter när de använder dessa tjänster.

Factors Affecting Consumers’ Intention to Use Online Music Service and Customer Satisfaction in South Korea

Minki Park EECS

KTH, Royal Institute of Technology Stockholm, Sweden

[email protected]

ABSTRACT

This study aims to investigate factors that affect consumers’ behavioral intentions to use online music services and the extent to which users of these services are satisfied with their experience. It also seeks to clarify the relationship between customer satisfaction and repurchase intention. The research framework of this thesis is built from the extended united theory of acceptance and use of technology (UTAUT2), and the data from an online survey in South Korea was analyzed using SPSS. The study suggests that the most important factors among users of these services are usefulness, hedonic pleasure, and price value. Customers will be satisfied with the service if they believe that it provides useful functions and amusement, and satisfied customers are likely to purchase the service again. It should be possible to efficiently use the results of this research to establish consumer-centric and efficient marketing strategies within aspects of the online music business and to better understand the behavior of consumers when using these services.

INTRODUCTION

The development of the internet and advances in communication and information exchange that came with it has led to a new world. Developments in information technology mean that the 21st century is often thought of as a new digital age. Digital technology is continually changing many aspects of our lives, including our patterns of music consumption. The music industry is evolving faster than ever. In 2018, the global recorded music market grew by 9.7%, the fourth consecutive year of growth, and streaming revenue grew by 34.0% and accounted for almost half (47%) of global revenue [11]. This suggests that the music industry is in a transitional stage; while physical products still exist and provide a large share of the industry’s revenue, consumption is moving away from physical towards digital, and consequently from ownership of products to access [34]. Digitalization has made music—

a product that used to be entirely physical—into an intangible information product.

South Korea has increasingly embraced online music services as the main source of music. About 10 million people in Korea subscribe to an online music service, and it is the world's sixth-largest music streaming market, following the U.S., Japan, the U.K., Germany, and France [13]. In terms of ratio, 41 percent of internet users in South Korea subscribe to an online music service which is the largest proportion globally [14].

In Korea, domestic online music services that seem to understand the demands of Korean users occupy most of the market share. Apple Music and Google Music launched their services in Korea in 2016, but they have had limited success in recent years while the services are popular globally. The reason for this failure is considered to be a lack of understanding about Korean users’ music consumption patterns. Korean users tend to listen to Korean songs most often [18], but the services did not have enough Korean songs because of licensing issue. Also, the services provided a radio streaming function as their main feature, but Korean users were not used to this type of concept – i.e. not knowing which songs would be played next. However, the licensing issues around Korean songs were solved in 2019, and users have now had time to get used to the concept of radio streaming. At the same time, new domestic music services are entering the market, and global giant music services are also set to enter. Spotify, the world's largest music streaming platform, is preparing to launch its service in Korea this year and Google also has a plan to launch YouTube Music Premium in Korea soon.

Based on this situation, it is vital for online music services to understand their users and to have a remarkable product and marketing strategy in order to stay competitive. Therefore, this research intends to discover the motivating factors behind music service usage intentions and the factors that affect customer satisfaction, and, by extension, the relationship between customer satisfaction and repurchase intention.

Research Question:

What are the main motivations for using an online music service? Which factors affect customer satisfaction? And does customer satisfaction affect repurchasing intention?

This study uses the extended unified theory of acceptance and use of technology (UTAUT2) as a basis for the research model. The UTAUT2 was chosen because it is designed based on the consumer’s context and is more extensive and performs statistically better than its predecessors such as the technology acceptance model (TAM), and UTAUT [3]. An online survey was conducted to collect the primary data, and the collected data provides the basis of empirical evidence for hypothesis testing. The data was analyzed using the Statistical Package for Social Science (SPSS).

BACKGROUND

Market Status in Korea

Online music service is defined as the platform that enables users to listen to digital music through internet access. In the Korean music market, more than ten online music services are available, and the competition is very fierce. A survey carried out by Korean Click shows a clean sweep of the market by local platforms. Melon has the largest share at 40.3 percent with around 3.8 million monthly active users as of December 2019. This is followed by Genie Music at 24.6 percent with 2.57 million, FLO at 18.5 percent, and Vibe, Naver Music and Bugs at about 3 percent, respectively [18]; all of these are local services. The competition is getting more intense as new services have steadily entered the market while existing services have developed new functions and conducted various marketing activities. The third and fourth ranked music services (FLO and Vibe) are new services launched in 2018, and the ranking from the second place keeps changing while the first placed service (Melon) is shrinking its market share.

To satisfy the variety of needs of customers, there are usually several subscription and payment methods available so customers can enjoy the service they need with their preferred purchase option. Because Korean users tend to have their favorite songs stored on their devices [18]– despite usually having internet access that would make listening online possible – a hybrid streaming-plus-download package (as opposed to just streaming) is a popular model.

Literature Review

Previous User Acceptance Theories

Online music services can be categorized as information systems (IS), and the grand theory from which most of the theories in consumer adoption of IS are derived from is the theory of reasoned action (TRA) by Fishbein and Ajzen (1975). TRA originates from the field of psychology and it has been used as a base theory for many user acceptance theories. Fishbein and Ajzen (1975) were the first to theorize the strong relationship between individuals’ intentions and their actual behavior. Intentions are assumed to capture the motivational factors that influence a behavior, and behavior is defined as an individual’s observable response in a given situation with respect to a given target [10]. The theory contends that human behavior is the result of intention, which is affected by two determinants - attitudes and subjective norms [10]. Attitudes refer to the way people feel towards a particular behavior, and subjective norms refer to the way perception of relevant groups or individuals such as family members or friends may affect one’s performance of the behavior [10]. According to the TRA, if people evaluate the suggested behavior as positive (attitude), and if they think their significant others want them to perform the behavior (subjective norm), this results in a higher intention and they are more likely to do so.

Other theories often applied to study the use of IS services and technologies are the theory of planned behavior (TPB) by Ajzen (1991), the technology acceptance model (TAM) by Davis (1989), and the unified theory of acceptance and use of technology (UTAUT and UTAUT2) by Venkatesh et al. (2003, 2012) [9].

The Theory of Planned Behavior (TPB)

The theory of planned behavior (TPB) is a social and behavioral sciences theory that predicts and understands human behavior. The theory was developed from the TRA to improve on the predictive power of the TRA. Even though a high correlation of attitudes and subjective norms to behavioral intention, and subsequently to behavior has been confirmed in many studies [27], a counter argument against the strong correlation between behavioral intention and actual behavior has also been proposed. As the results of some studies show that, because of circumstantial limitations, behavioral intention does not always lead to actual behavior. Namely, since behavioral intention cannot be the exclusive determinant of behavior where an individual’s control over the behavior is incomplete, Ajzen introduced the TPB by adding a new component,

“perceived behavioral control”, which is defined as an individual's perceived ease or difficulty of performing the particular behavior. By this, he extended the TRA to cover non-volitional behaviors for predicting behavioral intention and actual behavior. In summary, the TPB postulates that individuals behave rationally and that their behavior is guided by three factors – attitude, subjective norms, and perceived behavioral control [1].

The Technology Acceptance Model (TAM)

The technology acceptance model (TAM), which is one of the most influential extensions of the TRA, is an IS theory that models how users come to accept and use a technology. TAM brought in two new theoretical constructs: perceived usefulness and perceived ease of use. Perceived usefulness refers to the extent that an individual believes that using a certain technology will help them better perform a task compared to their performance without the technology [5]. Perceived ease of use refers to their belief in the effortlessness of the technology. The logic behind perceived ease of use is that even though a person believes that a technology would be useful for them, they might not adopt it if they perceive that using the technology would demand too much effort. Even though there are questions around a person's rational choice process, such as from Pierre Bourdieu, who suggests that social agents operate according to an implicit practical logic and bodily disposition rather than rationally-constructed intention – when it comes to using a technology, the argument that the stronger the intention to engage in a behavior the more likely it is to be performed [30] seems convincing. Using a technology cannot, after all, occur automatically or without intention.

UTATUT and UTAUT2

This study uses the unified theory of acceptance and use of technology (UTAUT) —more specifically the UTAUT2—as a basis for the research model. UTAUT2 is an extension to UTAUT, which was developed by Venkatesh et al. [31]. UTAUT has been widely used to study the use and adoption of numerous technologies in both organizational and non-organizational contexts [32]. However, the theory was primarily built to study technology use and adoption in a corporate environment, which led Venkatesh et al. to develop the UTAUT2, which applies to a consumer context [32].

UTAUT is a technology acceptance model formulated by Venkatesh et al. [31]. The UTAUT research model was built on previous literature and theoretical models that studied the use and adoption of new information technologies.

Venkatesh et al. integrates eight theories on technology adoption and provides a comprehensive view of the factors related to users' adoption behavior. In this model, the user is originally defined as an individual in an organization. The theory concludes that behavioral intention is a strong predictor of actual use behavior and puts forward four key determinants of behavioral intention and use behavior: performance expectancy, effort expectancy, social influence, and facilitating conditions. In this model, gender, age, experience, and voluntariness of use are the moderators that affect usage of technology [31].

UTAUT2 differs from its predecessor by adding three new key determinants to the model and leaving out the moderator of voluntariness. The theory focuses on explaining IS adoption of consumers. The reason for leaving out voluntariness from the moderators is that in a consumer context, users have no organizational mandate to use a certain technology and most consumer behaviors are entirely voluntary. This altered user definition reformulates the seven determinants from the perspective of the consumer, instead of defining them from the perspective of the employees of an organization [32]. The three new determinants in the UTAUT2 model are hedonic motivation, price value, and habit.

Research Model and Hypothesis Development

The research model is constructed from the items used in UTAUT2 and extended by adding customer satisfaction and repurchase intention. This section explains each of the constructs in detail and presents the research model and the hypotheses of this study.

Behavioral Intention

The effect of Behavioral Intention on actual behavior stems from the basic concept underlying user acceptance research. As mentioned above, the link between behavioral intention and usage has been proved by multiple user acceptance studies and it is the key factor of usage in several theories. Considering the strong link between behavioral intention and actual behavior in many of the previous user acceptance studies, one can assume that there will be a corresponding link in the context of online music services. In this study, behavioral intention is defined as the degree to which a person has formulated conscious plans to perform or not to perform some specified future behavior related to an online music service [31].

Customer Satisfaction

According to Roberts-Lombard, Customer Satisfaction is defined as the degree to which a business's product or service performance matches up to the expectation of the customer [25]. He also states that if the performance matches or exceeds the expectation, then the customer is satisfied. However, if the performance is below par, then the customer is dissatisfied. In this study, customer satisfaction is defined as the degree to which a person is satisfied with the online music service they use. Effort Expectancy

Effort Expectancy is “the degree of ease associated with consumers’ use of technology” [32]. It includes factors such as perceived ease of use and complexity, and perceived ease of use is defined as “the degree to which a person believes that using a particular system would be free of effort” [35]. In this study, it is defined as the degree of ease associated with the use of the online music service. H1-1 Effort Expectancy is positively related to behavioral intention to use an online music service. H2-1. Effort Expectancy is positively related to customer satisfaction. Hedonic Motivation

Hedonic Motivation is “the fun or pleasure derived from using a technology” [32]. This is attributed to the fact that an enjoyable experience and fun in using a technological-based service motivates users. Additionally, hedonic motivation such as enjoyment has been found as an important driver of a technology’s adoption since it helps to trigger a positive attitude among users [26]. In this study, it is defined as the degree to which an individual perceives fun and enjoyment when using an online music service. H1-2. Hedonic Motivation is positively related to behavioral intention to use an online music service. H2-2. Hedonic Motivation is positively related to customer satisfaction. Performance Expectancy

Performance Expectancy is described as “the degree to which using a technology will provide benefits to consumers in performing certain activities” [32]. Chu and Lu defined it in the context of online music services as “the degree to which the consumer believes that listening to music online would fulfil the certain purpose.” [4]. In this study, Chu and Lu’s definition is used so it is defined as the degree to which the consumer believes that listening to

music via an online music service would fulfil a certain purpose. H1-3. Performance Expectancy is positively related to behavioral intention to use an online music service. H2-3. Performance Expectancy is positively related to customer satisfaction. Facilitating Conditions

Facilitating Conditions is described as “the consumers’ perceptions of the resources and support that they have available to perform a certain behavior” [32]. In this context, facilitating conditions for using online music services include customer support, mobile devices, and internet connection. In this study, it is defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of an online music service. H1-4. Facilitating Conditions is positively related to behavioral intention to use an online music service. H2-4. Facilitating Conditions is positively related to customer satisfaction. Price Value

Price Value is conceptualized as “the cognitive trade-off between the perceived benefits received from using the application and the monetary cost for using it” [8]. If a consumer perceives that the benefit and advantages gained through the usage of an online music service outweigh the price they paid, the consumer will have the intention to buy and use it. In this study, price value is defined as the degree to which an individual's satisfaction is greater than the cost of buying and using the online music service. H1-5. Price Value is positively related to behavioral intention to use an online music service. H2-5. Price Value is positively related to customer satisfaction. Social Influence

Social Influence is “the extent to which consumers perceive that important others such as family and friends believe they should use a particular technology” [32]. In this study, it is defined as the degree to which an individual feels that the other important person influences him or her to use their online music service. H1-6. Social Influence is positively related to behavioral intention to use an online music service. H2-6. Social Influence is positively related to customer satisfaction.

Habit

Habit is defined as “situation-specific sequences that are or have become automatic so that they occur without self-instruction” [20]. In this study, it is also defined as a repeated behavioral pattern of using an online music service that automatically occurs outside conscious awareness [15]. H1-7. Habit is positively related to behavioral intention to use an online music service. H2-7. Habit is positively related to customer satisfaction.

Repurchase Intention

Repurchase Intention is defined as “the customer's decision to engage in future activities with the retailer or supplier” [10]. In this study, repurchase intention is defined as the individual's judgement about repurchasing a designated online music service, considering his or her current situation and likely circumstances. H3. Consumer’s satisfaction is positively related to customer repurchase intention. A visual representation of the research models is depicted in “Figure 1”, and the hypotheses are listed in “Table 1”.

Hypothesis H1: The seven determinants are positively related to behavioral intention to use an online music service.

H1-1 Effort Expectancy is positively related to behavioral intention to use an online music service. H1-2 Hedonic Motivation is positively related to behavioral intention to use an online music service. H1-3 Performance Expectancy is positively related to behavioral intention to use an online music service. H1-4 Facilitating Conditions are positively related to behavioral intention to use an online music service. H1-5 Price Value is positively related to behavioral intention to use an online music service. H1-6 Social Influence is positively related to behavioral intention to use an online music service. H1-7 Habit is positively related to behavioral intention to use an online music service.

H2: The seven determinants are positively related to customer satisfaction. H2-1 Effort Expectancy is positively related to customer satisfaction when using an online music service.

H2-2 Hedonic Motivation is positively related to customer when using an online music service. H2-3 Performance Expectancy is positively related to customer satisfaction when using an online music service. H2-4 Facilitating Conditions are positively related to customer satisfaction when using an online music service. H2-5 Price Value is positively related to customer satisfaction when using an online music service. H2-6 Social Influence is positively related to customer satisfaction when using an online music service. H2-7 Habit is positively related to customer satisfaction when using an online music service.

H3: Consumer’s satisfaction is positively related to customer repurchase intention

Table 1. Hypothesis

Figure 1. Research Model

METHOD

Measurement

In this study, all information was gathered and collected from the primary data. The primary data was collected using a questionnaire regarding behavioral intention to use online music services in Korea, and the set of questions was designed according to the original UTAUT model, UTAUT2 model, and other research related to online music adoption [5, 7, 21, 32]. The questionnaire consisted of 39 items that were rated on a 7-point Likert scale varying from strongly agree to strongly disagree. The questionnaire was reviewed and pretested by five individuals working in the field of online music services. Minor adjustments were made in order to simplify questions and reduce misunderstandings. The final survey items are listed in Appendix 1. The original survey was in Korean as the purpose of this study is to understand Korean users. It was then translated into English.

Data Collection

The data was collected via an online survey, which was distributed to users of the popular online community in Korea (Naver), to students in a local high school and university, and to office workers in a local company. In total 137 completed questionnaires were returned prior to data analysis. After the initial data cleaning, 105 valid responses were used for further analysis. The sample size looks quite small, but sufficient to perform a structural equation model analysis. Gefen et al. and Ding et al. state that 100 to 150 is the required minimum sample size to conduct a structural equation modelling analysis [8,10].

Statistical Analytics Methods

The relationship of the proposed model and the properties of the scale were analyzed using the Statistical Package for Social Science (SPSS). The statistical techniques were used according to commonly accepted research assumptions. The tests used included reliability analysis, correlation analysis, multiple regression analysis, and simple regression analysis.

DATA ANALYSIS AND RESULTS

Demographics

There were 69 female participants, which constituted the majority (65.7%), and 36 males (34.3%) in this study. The respondents were in the age ranges 10-19 (4.8%), 20-29 (27.6%), 30-39 (51.4%), and 40-49 (16.2%).

Figure 2. Frequency analysis for demographics

Reliability Test

It is important to study the properties of measurement scales and the terms that compose the scales. SPSS software was used in order to ensure that the variables in the model are reliable. The reliability test used most is Cronbach’s Alpha index. This is due to the interpretation as a correlation coefficient which ranges from 0 to 1. Furthermore, using the Cronbach’s Alpha index can determine whether the questionnaire is reliable, and whether the data can be used for further analysis. The acceptance level of Cronbach Alpha index should exceed 0.7 [2]. The findings show that all the constructs have the Cronbach’s Alpha above 0.7, so no item was eliminated.

Figure 3. Reliability Analysis

Correlation Analysis

Pearson correlation analysis was conducted to check if there is a linear relationship between the independent and dependent variables. Pearson correlation coefficient is a measure of the linear correlation between two variables, where 1 denotes total positive correlation, 0 means no correlation, and −1 is total negative correlation.

Figure 4. Correlation Analysis

“Figure 4” displays the results obtained from the correlation analysis, and it shows that all the independent variables have a relationship with the dependent variable, as the value of the correlation is different from 0. This indicates that between the independent and dependent variables the linear relationship required is present to proceed with multiple linear regression analysis. Also, since the correlation matrix shows that no variable exceeds the cut-off point 0.9, the problem of multicollinearity does not exist.

Multiple Regression Analysis

Regression analysis is a statistical tool for investigating the quantitative relationship between variables and can prove the relationship between an independent and dependent variable. Two multiple regression analyses were conducted to examine whether the independent factors (Effort Expectancy, Hedonic Motivation, Performance Expectancy, Facilitating Conditions, Price Value, Social Influence, Habit) affect the two dependent factors respectively; Behavioral Intention to use an online music service and Customer Satisfaction.

Figure 5. Regression Analysis Summary for variable Behavioral Intention

Based on “Figure 5”, it can be observed that this is a statistically significant regression model (F=35.035, p=.000). The 𝑅2 variable is 0.717 which represents 71.7 percent of the Behavioral Intention can be explained by each independent variable. Also, it has no multicollinearity problem (VIF<10), and this model is satisfied with the independency of standard residuals (Durbin-Watson (DW)=2.091).

The analysis shows that Hedonic Motivation (t=3.310, p<.01), Performance Expectancy (t=3.269, p<.01), Price Value (t=2.717, p<.01) and Habit (t=3.145, p<.01) are positively related to Behavioral Intention while Effort Expectancy, Facilitating Conditions, and Social Influence have no relation to it. Thus, H1-2, H1-3, H1-5, H1-7 were supported and H1-1, H1-4, H1-6 were not supported.

The value of B represents the increment in the dependent variable when a change is applied to the independent variable while other independent variables are constant. That is, for each one unit increase in Hedonic Motivation, Behavioral Intention will increase by 0.294 units while other independent variables remain constant. The other respective figures are 0.355 for Performance Expectancy, 0.163 for Price Value, and 0.220 for Habit. Beta(β) expresses the relative importance of each independent variable in predicting the dependent variable. The strongest variable, therefore, would be Performance Expectancy with a beta weight of .338, while the weakest would be Habit with a beta weight of .198.

Figure 6. Regression Analysis Summary for variable Customer Satisfaction

"Figure 6" also expresses that this regression model is statistically significant (F=21.004, p=.000), and has a meaningful power of explanation (𝑅2=.603) Also, it has no

multicollinearity problem (VIF <10), and has no autocorrelation in the residuals (DW=2.404).

The analysis shows that Hedonic Motivation (t=3.248, p<.01) and Performance Expectancy (t=2.169, p<.01) are positively related with Customer Satisfaction, Thus, H2-2 and H2-3 were supported. Furthermore, Hedonic motivation has a stronger effect (β =.330) while Performance Expectancy has .265 as β. Effort Expectancy, Facilitating Conditions, Price Value, Social Influence, and Habit variables have no relation with Customer Satisfaction, thus H2-1, H2-4, H2-5, H2-6, H2-7 were not supported.

Simple Regression Analysis

One simple regression analysis was conducted to examine whether the independent factor (Customer Satisfaction) affects the dependent factor (Repurchase Intention).

Based on “Figure 7”, there is no autocorrelation as DW is close to 2, and this model has 52.4% of explanation power (𝑅2=.524). There is a significant linear relationship between Customer Satisfaction and Repurchase Intention (t=10.645,

p<.01), which means H3 was supported. The B coefficient is statistically significant (B=.808), which means for each one unit increase in Customer Satisfaction, Repurchase Intention will increase by .808 units.

Figure 7. Simple Regression Analysis

The summary of research hypotheses is shown in "Table 2", below.

DISCUSSION

Performance Expectancy, which means the degree to which using a technology will provide benefits to consumers when performing certain activities, was the strongest determinant of Behavioral Intention to use online music services. This result shows that the potential users of online

Table 2. Summary of Results

Hypothesis Result H1: The seven determinants are positively related to behavioral intention to use an online music service. H1-1 Effort Expectancy is positively related to behavioral intention to use an online music

service. Rejected

H1-2 Hedonic Motivation is positively related to behavioral intention to use an online music service.

Accepted

H1-3 Performance Expectancy is positively related to behavioral intention to use an online music service.

Accepted

H1-4 Facilitating Conditions are positively related to behavioral intention to use an online music service.

Rejected

H1-5 Price Value is positively related to behavioral intention to use an online music service. Accepted H1-6 Social Influence is positively related to behavioral intention to use an online music

service. Rejected

H1-7 Habit is positively related to behavioral intention to use an online music service. Accepted H2: The seven determinants are positively related to customer satisfaction. H2-1 Effort Expectancy is positively related to customer satisfaction when using an online

music service. Rejected

H2-2 Hedonic Motivation is positively related to customer when using an online music service. Accepted H2-3 Performance Expectancy is positively related to customer satisfaction when using an

online music service. Accepted

H2-4 Facilitating Conditions are positively related to customer satisfaction when using an online music service.

Rejected

H2-5 Price Value is positively related to customer satisfaction when using an online music service.

Rejected

H2-6 Social Influence is positively related to customer satisfaction when using an online music service.

Rejected

H2-7 Habit is positively related to customer satisfaction when using an online music service. Rejected H3: Consumer’s satisfaction is positively related to customer repurchase intention Accepted

music services believe that they can use the online music service to listen to the music they want, easily find music that suits their tastes, and get relevant information. And because those activities are the basic purposes of using an online music service, it seems that Performance Expectancy was turned out to be the strongest determinant. This result supports the current concerns of the online music services themselves. Many services have put a lot of effort into content curation such as personalized recommendations and contextualized playlist in order to enable users to more easily discover music that is relevant to their tastes. Also, the services are also trying to quickly secure licensing rights to newly released music, something that has been shown to be of prime importance to users. Online music service providers should consider whether they have enough music sources that users can enjoy and whether the content they provide is useful to users. Performance Expectancy is also one of the factors that affect Customer Satisfaction. Because users regard an online music service as a method of listening to music, if they believe that an online music service fulfills that role then they will be willing to use this service and draw satisfaction from doing so.

Hedonic Motivation was the second strongest determinant of Behavioral Intention in this model. The finding implies that an online music service is an extremely hedonic IS system as Hedonic Motivation acts as a powerful indicator of Behavioral Intention. Simply put, people believe that using an online music service gives them pleasure, and that is why this determinant showed a strong relationship with Behavioral Intention. This result suggests that the service providers should try to increase the pleasure that users can derive from using their services. This means that not only the music itself, but also the content around the music should be very carefully considered. It suggests that online music services should try to provide other music related content such as music videos, interviews with artists, and recordings of concerts, all of which may positively contribute to a user's enjoyment of the service. In addition to this, Hedonic Motivation has the strongest influence on Customer Satisfaction. I think it is because listening to music is ultimately an act for pleasure, so if the users of online music services find the various features and functions in online music services fun, the users are then satisfied with the service.

Price Value also influences Behavioral Intention to use an online music service. This finding implies that music service providers should look for more ways to create value for their users and communicate the value of their service

to their consumers. It also reflects how music consumers are price sensitive– they can after all access music for free through several different channels such as free streaming services if they choose to. Katsternakes and Bi state that this is the only way that companies can differentiate themselves from each other when they all offer similar functions and similar music catalogues [17]. In response to such research, many music services are now offering price discounts, sometimes even for free, for the first few months in order to attract new customers, and a phenomenon that shows online music service providers are in fierce competition with one another. The interesting thing is that even though the price value has a significant effect on Behavioral Intention to use an online music service, this does not influence Customer Satisfaction. This implies that a reasonable price is helpful to attract users, but it is not an important factor in terms of customer satisfaction. This is because the average price of an online music service is around $7 per month and is payable for a majority of users, so consumers are willing to pay if the service is satisfactory.

Habit has been proven to significantly affect the Behavior Intention to use online music services. This finding implies that it is important for music services to create habits for their users so that they will frequently use the service. Continual use of the service, including unplanned use, constitutes habitual behavior. This result was expected – it is reasonable to assume that an experienced user of online music services will do so without problems. From a service provider's point of view, it can be a double-edged sword: if the customer is not fully satisfied with their service then they may transfer to another provider if they find the other service to be more useful and enjoyable. However, from a Customer Satisfaction point of view, habit has been confirmed to be an insignificant driver as it occurs unconsciously, and as such is difficult to connect with satisfaction.

Aside from these four variables, Effort Expectancy, Facilitating Conditions, and Social Influence were found to have an insignificant effect on both the user Behavioral Intention to use online music services and Customer Satisfaction.

The results pertaining to Effort Expectancy imply that users are knowledgeable in using the technology and are favorably disposed to do so, so the difficulty level of using the service is not a hindrance in the intention to use it. In this sense, the ease of online music services does not have a significant effect on Customer Satisfaction either. It might be because most respondents were relatively young, aged

between 20 and 40, and therefore are used to technology and capable of learning how online music services work.

Facilitating Conditions also proved to have an insignificant effect on Behavioral Intention. The result might be due to a sample that is predominantly experienced in using online music services. Another important factor to note is that Korea is a very technologically developed country, so consumers do not seem to have difficulties in using online music services in Korea where the internet and mobile connectivity is good. The result shows that providing facilitating conditions also does not have a significant effect on Consumer Satisfaction.

The results of this study indicate that Social Influence does not affect either the Behavioral Intention to use online music services or Customer Satisfaction. This implies that music consumers base their consumption methods on their own reasoning rather than on the opinions of others because listening to music is ultimately a matter of personal taste.

Finally, Customer Satisfaction has a significant effect on Repurchase Intention. The more users are satisfied with the online music service, the more they will have the intention to re-use that specific online music service.

CONCLUSION

The aim of this study was to examine factors that determine both the Behavioral Intention to use online music services and the degree of Customer Satisfaction when using them, as well as the relationship between Customer Satisfaction and Repurchase Intention in South Korea. This study uses well-known theories from the field of IS adoption to form a research framework and hypotheses.

The research questions of this study were: What factors lead consumers to use online music services? What factors affect consumer satisfaction? And does customer satisfaction affect repurchase intention?

Three specific objectives were observed as follows. First, the study found out that Performance Expectancy, Hedonic Motivation, Price Value, and Habit are determinants of consumers’ Behavioral Intention to use online music services and thus are factors that lead consumers to use these services. Other hypothesized determinants of Behavioral Intention to use online music were Effort Expectancy, Facilitating Conditions, and Social Influence. These three determinants were not found to have an influence on people’s intent to use online music services. Secondly, only two of the hypotheses were supported and

proved to affect Customer Satisfaction. The factors that were found to affect customer satisfaction are Performance Expectancy and Hedonic Motivation. Further, this study has rejected five hypotheses, as the statistical analysis for Effort Expectancy, Facilitating Conditions, Price Value, Social Influence, and Habit showed that these factors do not statistically affect customer satisfaction when using online music services. Finally, it was clarified that Customer Satisfaction is a significant factor when it comes to Repurchase Intention.

From a practical standpoint, the result provides important insight and implication for music service providers. They should be able to better understand customer needs and the factors affecting users’ intention to use online music services, and customer satisfaction. Thus, this research helps them to provide more valuable services to their users by building up their business strategies focusing on the highlighted significant factors. Online music service providers could refer to this study for reference when developing strategies and conducting marketing activities.

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APPENDIX. 1

Table 3. Questionnaire items

Construct Item

Effort Expectancy

∙It is easy for me to become skillful at using online music services. ∙Learning how to use an online music service is easy for me. ∙I find online music services easy to use. ∙My interaction with online music service is clear and understandable.

Hedonic Motivation ∙Using an online music service is fun. ∙Using an online music service gives me great pleasure. ∙Using an online music service is very entertaining.

Performance Expectancy ∙Using an online music service is useful to listen music. ∙Using an online music service makes me find music effectively/ productively. ∙It is helpful to get music related information by using online music service. ∙I can find the music quickly when I use an online music service.

Facilitating Conditions ∙I have the resources (money/ device) necessary to use online music services. ∙I have the knowledge necessary to use online music services. ∙I use network service necessary to use online music service smoothly. ∙I can solve the problem with others' or the service provider's help when I have a problem to use a service.

Price Value ∙The price of online music services is reasonable. ∙Online music services are good value for money. ∙At the current price, online music services provide a good value.

Social Influence ∙People who are important to me think that I should use online music service. ∙I tend to use online music service by others' recommendation. ∙People around me help me (or will help me) to use online music service.

Habit ∙I am addicted to using online music services. ∙I must use online music service ∙I use online music service without any specific purpose.

Behavioral Intention ∙I intend to use online music services in the near future (or keep using). ∙I predict that I will use online music service in the near future (or keep using). ∙I plan to use online music service in the near future (or keep using).

Customer Satisfaction ∙I am overally satisfied with the online music service that I used. ∙I am more satisfied than I expected before using the service. ∙I will recommend this online music service to others.

Repurchase Intention ∙I will revisit and use this online music service. ∙I will repurchase this online music service. ∙In case that I stop using online music service now and use online music service again later, I will consider this service firstly.

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