Musical Habits and Smartphone Addiction of College Students: Mediating Role of Self-Control

Article History: This study uses theories of habit and self-control to explore the relationship between musical habits and smartphone addiction among college students. Our findings reveal that self-control mediates this relationship, with individual differences playing a significant role. While musical listening habits, impacted by personal genre preferences, positively impact self-control, active music engagement, through playing an instrument and/or creating music, negatively affects it. We suggest using interventions to promote mindful music engagement to mitigate smartphone addiction. These insights contribute to understanding how music habits influence digital behaviors and offer practical strategies for fostering healthier smartphone use among college populations.


Introduction
With the advancement of digital technology, smartphone use is prevalent all over the world.Statista (2022) predicted that 7.7 billion of the population around the world would use smartphones by 2027.Many smartphone users stated that they could not live without a smartphone (Observatory of Digital Uses, 2019, p. 9).One of the downsides of the prevalence of smartphones is smartphone addiction, especially among college students (Chen et al., 2019; Tayhan Kartal and Yanbanci Ayhan, 2020;Lin et al., 2016;Shahjehan et al., 2021;Yilmaz et al., 2023).Prior research has indicated that smartphone addiction can lead to negative impacts such as detrimental consequences in personal life, health, cognitive performance, and workplace performance (see for example, Charoensukmongkol, 2016;Chidambaram et al., 2022;Geng et al., 2021;Kayis et al., 2021;Chandrasekaran et al., 2019;Nongpong & Charoensukmongkol, 2016;Shahjehan et al., 2021;Zhao et al., 2021).
Meanwhile, music engagement (Bowman, 2004;Upadhyay et al., 2017;Wöllner et al., 2010), which includes two types of musical habits (music listening and music playing and creating), has been integrated into the fabric of people's daily lives in recent years (Statista, 2019a and b).Nearly 70 percent of Americans listen to music daily, with the younger generations having even higher percentages (Statista, 2019a).For example, the revenue from paid subscriptions of streaming music was US$60.4 million in 2019 compared to US$10.8 million in 2015 (Perez, 2020).The popularity of music production including music playing and creating is manifested in a growing market in musical instruments with US$4,287 million in revenue and a 3.2% growth in 2019 (Statista, 2019b).Further, the music production software market is projected to grow by US$ 368.15 million during 2020-2024 ("Music Production Software Market Analysis" 2020).Prior research has shown that social media use and online gaming are substantial predictors of smartphone addiction (Liu et al., 2016;Wang & Lee, 2020;Yang et al., 2022).Given the increasing use of online music streaming and production (Yang et al., 2020), we raise the question of the relationship between musical habits and smartphone addiction.
Given the number of people who are addicted to smartphones (Kuem et al., 2021), scholars should seek to identify the antecedents to reduce the innumerable harms caused by this addiction.So far, scholars have agreed that technology use habits and self-control are two main predictors of technology addiction (Soror et al., 2015;Turel & Bechara, 2016;Turel & Qahri-Saremi, 2016;van Deursen et al., 2015).For example, Turel & Bechara (2016) found that online social network use positively predicted online social network addiction and self-control negatively affected it.Also, Turel & Qahri-Saremi (2016) found that selfcontrol is not only negatively associated with problematic uses of Facebook, but also negatively predicted Facebook use habits.Other research has also shown that smartphone use habits and selfcontrol were positively related to smartphone use frequency and duration (Soror et al., 2015) and are thus significant contributors to smartphone addiction (van Deursen et al., 2015).In other academic fields, researchers have identified self-control as a mediator of the relationship between predictors (e.g., envy, physical activity) and smartphone dependence and addiction (Zhang & Xiang, 2022;Zhong et al., 2020).Although evidence has been provided to show a close relationship between technology use habits and self-control and technology addiction, the impact of other life habits on smartphone addiction remains unexplored.
Expanding on previous research, this study uses a survey to study musical habits and smartphone use among college students.We aim to examine how musical habits interact with self-control in influencing smartphone addiction.
This study offers both theoretical and practical contributions.Theoretically, it employs dual-systems theories to build a research model of the relationships between musical habits and smartphone addiction and the mediating role of self-control (see Figure 1).This expands the Information Systems literature on the negative aspects of technology adoption and usage, while also contributing to interdisciplinary research at the intersection of music and Information Systems.Practically, the findings of this study can be leveraged by practitioners to address college students' smartphone addiction.For instance, they can utilize music as a tool to enhance self-control, such as developing and implementing music-based applications aimed at reducing smartphone dependence.The remainder of this study is organized as follows.First, we present the theoretical background and propose the hypotheses.Next, we discuss the research methodology containing participants, measures, and analytical approach.Finally, we display the results and discuss the implications.

Smartphone Addiction
Technology addiction, noted by Turel (2011), is the maladaptive dependence on technology use with accompanying negative consequences (e.g., salience, withdrawal, conflict, relapse and reinstatement, tolerance, and mood modification).Also, technology addiction, including smartphone addiction, is considered a category of behavioral addiction because of such similarities as continuous uncontrollability (Hooper & Zhou, 2007).Based on prior studies (Liu et al., 2016;Wang & Lee, 2020;Yang et al., 2022), we define smartphone addiction as a continuously maladaptive and uncontrollable smartphone use at the expense of negative physiological, psychological, and behavioral consequences.We recognize that this definition is by no means fixed as the terminology of smartphone addiction may change given future research and associated conceptual adaptations (Montag et al., 2021).

Musical Habits
Habits are often interpreted as 'learned sequences of acts that become automatic responses to specific situations which may be functional in obtaining certain goals or end states' (Verplanken et al., 1997, p. 540).According to the review by Wood and Rünger (2016), habits are goal-directed actions that reflect associative learning, which in turn affects self-control.Since musical habits and musical engagement are interchangeably used (Bowman, 2004;Upadhyay et al., 2017;Wöllner et al., 2011), we define musical habits as learned musical acts to achieve individual goals or states, and we follow Vanstone et al. (2016) by categorizing musical habits into (1) musical listening and (2) musical playing and creating.
Musical habits, whether listening or playing and creating, are goal-achieving actions.People listen to music daily to regulate their emotions (Bautch, 2020).For those who play instruments and/or create music, there is a desire to improve their musical skills.When faced with distractions, a choice has to be made between spending time on the music or allowing oneself to be distracted.As stated earlier, there is an increasing number of online music streaming and music production options that are likely to drive increased smartphone usage.This leads us to ask how musical habits like listening and creating affect smartphone usage and addiction.

Dual-Systems Theory
We examine the impact of musical habits on smartphone addiction based on the dual-systems theory (see Evans, 2008 for a review).According to the theory and existing literature (e.g.Everitt et al., 2008;Kahneman, 2003;Stanovich & West, 2000), humans process information through two structurally distinctive systems: System 1 (e.g.habit) and System 2 (e.g.self-control).System 1 is holistic, automatic, goal-oriented, emotionally charged, and relatively fast, while System 2 is rule-based, analytical, controlled, and relatively slow.Moreover, the two systems interact with each other to generate various behaviors.In recent years, some scholars have claimed that habits can influence self-control (see Carden & Wood 2018, for a review).For example, if a habit achieves goals, it can reduce the need for self-control and thus enhance desired behaviors or performance (Carden & Wood, 2018).
The dual-system theory has been particularly useful in explaining the etiology of problematic behaviors, such as problem gambling, overeating, problematic drinking, and smoking (Evans, 2008).IS scholars have used it to examine and explain the etiology of technology addiction (Gong et al., 2019;Ning et al., 2021;Osatuyi & Turel, 2018;Soror et al., 2015;Turel and Qahri-Saremi, 2016).For instance, Turel and Qahri-Saremi (2016) found that both Facebook use habits and self-control significantly predicted problematic use of Facebook.They also found that self-control is negatively associated with Facebook use habits.Given the similarities between smartphone addiction and these problematic behaviors (e.g., behavioral addiction and technology addiction), we sought to apply the dual-system theory to explain how musical habits interact with self-control to influence smartphone addiction.

Model Development
In this paper, we explore how musical habits interact with self-control to impact smartphone addiction.Per the dual systems theory, musical habits are System 1 and self-control is System 2. While acknowledging that habits include both automaticity and goal pursuit, musical habits as defined in our study are focused more on goal pursuit.Figure 1 presents the theoretical model in this study.The model suggests that music listening habits only influence self-control with musical preference.The model then indicates that musical playing and creating habits can lower selfcontrol.Finally, the model posits that self-control will hurt smartphone addiction.

Habits and self-control
According to the dual-systems theory, habits can interact with self-control (e.g.Evans, 2008).Guided by this theory, Wood and Rünger (2016) set up an approach to address the effect habits have on self-control.This approach asserts that if habits can generate positive affect (i.e., emotion or mood), people will deliberately monitor their repetitive behaviors and infer that they can achieve their goals.It is such positive affect and the associated goal inference that links the positive relationship between habits and self-control.Similarly, if habits generate negative effects, people will have low self-control.
The strength model of self-control states that people have limited internal resources such as willpower that have an association with self-control (Baumeister et al., 2007;Muraven & Baumeister, 2000).These internal resources will be exhausted following physical and mental exertion, which is referred to as ego depletion.Baumeister et al (1994) proposed that emotional distress contributes to the self-control breakdown.Baumeister (2002) also found that stress and coping with stress can deplete one's internal resources that are used to implement self-control.
To recover from ego depletion, Tice et al. (2007) found that the manipulated positive affect (i.e., emotion or mood) after an ego depletion boosts self-control on subsequent tasks because the positive affect can return a person to a neutral physiological state from the negative affect.
From the dual-systems theory and the strength model of self-control, we conclude that the impact of habits on people's self-control depends on the effect that is associated with a habit and how people deal with the effect.That is, if a habit generates a positive effect, it can increase people's self-control, or vice versa.Of course, a habit may not be associated with self-control if the effect is neutral.Meanwhile, if people can cope with their affect well even when they are in negative affect, they can increase their self-control, or vice versa.

Music Listening Habit, Music Preference, and Self-Control
In the discipline of music, the impact of music on stress and affect is dependent on many factors, including music preference and individual differences (Juslin & Sloboda, 2001;Swaminathan & Schellenberg, 2015).Music preference refers to an individual's predisposition toward a specific type or style of music related to music characteristics such as tempo (i.e., the speed at which a piece of music is played) and mode (i.e., a scale pattern with unique qualities and sound) (Vanstone et al., 2016).Generally, happy music is played in a fast tempo and major mode, whereas sad music is characterized by a slow tempo and minor mode (e.g., Pallesen et al., 2005).Although music researchers have only examined relationships between people's interest in listening to happy or sad music and the associated effects, we use this limited literature to develop hypotheses on the impact of music listening habits and music preference on self-control.
Happy music can generate positive effects and enhance energy levels because it activates the ventral and dorsal striatum that are involved in the reward experience.Sad music typically leads to negative effects since it involves the activation of the hippocampus and amygdala which are important for memory and emotion processing (Mitterschiffthaler et al., 2007;Saarikallio and Erkkilä, 2007).Some researchers claimed that sad music can also induce a pleasant emotion (Kawakami et al., 2013) when the listeners have a sweet anticipation from the sad music (Huron, 2006) and/or evaluate it as art and thus have enjoyment from listening to it (Koelsch, 2013).Most people listen to music either to regulate their affect or to enjoy it as an art.Often, they prefer listening to happy music over sad music, which results in positive affect and less stress.Those who like to listen to sad music may experience positive effects and less stress when they evaluate the music as art and have sweet anticipation from it; otherwise, they will experience negative effects and more stress.For people with issues like being chronically stressed and having long-term negative effects), listening to happy music can reduce their stress (Chafin et al., 2004;Getz et al., 2012).However, this group of people seems to prefer listening to sad music, which can worsen their situation and ultimately increase their current negative affect instead of alleviating it (Millgram et al., 2015;Yoon et al., 2019).
Given the above evidence, we expect that the impact of musical listening habits on self-control depends on both musical preference (happy and/or sad) and individual disposition.Therefore, it would suggest that music listening habits are not related to self-control.We also expect that music listening habits with musical preference overall generate more positive effects than negative effects.We thus hypothesize that H1: Music preference moderates the relationship between music listening habits and self-control, such that the positive effect of music listening habits on self-control is stronger when music preference is strong.

Music Playing and Creating Habit and Self-Control
Some may think that people such as musicians who play and/or create music are happy as they are exposed to music frequently that alleviates their stress and induces positive moods.However, research has shown that musicians tend to be more prone to negative thought patterns (e.g.Jones et al., 2014) and depression (e.g.Young et al., 2013) because they have higher levels of stress associated with music playing and creation than those who do not perform or create music.For example, Getz et al. (2012) found that participants who had more experience with music training suffered from long-term stress and had less optimism which served as a buffer to stress.Similarly, Roy et al. (2016) showed that college students' musical ability was positively related to short-term stress.
Moreover, musicians or people with great musical ability tend to analytically and intelligently listen to music rather than listening to regulate distressful emotions (Getz et al., 2011).Thus, ego depletion will occur because they exert a great amount of

International Journal of Business and Social Science Research
Vol: 5, Issue: 4 March/2024 DOI: http://dx.doi.org/10.47742/ijbssr.v5n4p2https://ijbssrnet.com/index.php/ijbssrmental effort in analyzing the music while listening to it, which lowers their self-control.Also, as mentioned above, some of them may use music to regulate their emotions and moods but in a negative direction because they are inclined to listen to and/or play and create sad music (Swaminathan and Schellenberg, 2015).Such emotional distress due to sad music exposure accelerates the decrease of their self-control.Thus, we hypothesize that H2: Music playing and creating habits is negatively related to self-control.

Self-Control and Smartphone Addiction
In recent years, an increasing number of studies on smartphone addiction has also shown that individuals' deficits in self-control are typically associated with the risk of developing smartphone addiction (Jeong et al., 2016;Kim et al., 2016;Mahapatra, 2019;Zhang & Xiang, 2022;Zhong et al., 2020).In these behavioral studies, self-control was either a predictor or mediator of smartphone addiction.For example, Mahapatra (2019) found that adolescents' self-control was negatively related to their smartphone addiction.Also, college students with poor self-control due to physical inactivity had higher smartphone dependence (Zhong et al., 2020).With the aid of neurological tools, researchers also found that the dysfunction of neural correlates such as orbitofrontal cortex (OFC; impulse control) and left anterior cingulate cortex (ACC; affect regulation) are linked to internet addiction (Lin et al., 2012;Zhou et al., 2011).Thus, we hypothesize that H3: Self-control is negatively related to smartphone addiction.

Participants and Research Design
A total of 290 college students (Mean age = 21.14, SD = 3.41) from a southern university in the U.S. participated in this study.The participants earned class credit for their engagement by filling out the online consent form and self-report surveys that measured their demographic information (e.g., age and gender), social desirability, two types of musical habits, smartphone addiction, self-control, and music preference.The participants consisted of 39% male and 61% female; 2.5% were freshmen, 4.1% were sophomores, 25.3% were juniors, 48.6% were seniors, and 19.5% were graduate students.

Common Method Bias
Common method bias is a potential threat to the validity of a survey that uses single respondents for both the independent and dependent data (Podsakoff et al., 2003).We addressed this issue with the following methods.First, we collected the data online via Qualtrics using a multi-wave approach (e.g., Turel and Qahri-Saremi, 2016).Specifically, our participants filled out a survey measuring their demographic information, music listening habits, music playing and creating habits, and music preferences.Two days later, we sent them another survey that measured their social desirability, physical activity participation, self-control, and smartphone addiction.Second, we informed participants of the importance of the study and provided them with an explanation of some terminologies that could be confusing.Third, we used the marker variable technique (Malhotra et al., 2006) by incorporating physical activity participation questionnaires (Prochaska et al., 2001) in the survey.The analysis showed that the average correlation between participants' physical activity participation and the main variables was 0.07.It also indicated that the adjusted correlations between the main variables remained significant after including the marker variable in the partial least squares structural equation modeling (PLS-SEM).Thus, we are confident that the common method bias was not an issue in the current study.

Measures
Appendix A displays the items of measures used in our study.We used the 10-item multidimensional scale (i.e., daily-life disturbance, withdrawal, cyberspace-oriented relationship, overuse, and tolerance) developed by Kwon et al. (2013) to measure smartphone addiction.Example items are "Missing planned work due to smartphone use" and "Using my smartphone longer than I had intended".This scale is rated on a 6-point Likert scale from 1 "Strongly disagree" to 6 "Strongly agree".The internal consistency reliability coefficient of the scale is 0.91.
Self-control was measured with the 8-item two-factor scale (i.e., restraint and impulsivity) by Maloney et al. (2012).Example items of the scale are: "I have a hard time breaking bad habits" and "Pleasure and fun sometimes keep me from getting work done".These items are rated on a 5-point Likert scale ranging from 1 "Not at all like me" to 5 "Very much like me".The internal consistency reliability coefficients are 0.72 for restraint and 0.73 for impulsivity.

International Journal of Business and Social Science Research
Vol: 5, Issue: 4 March/2024 DOI: http://dx.doi.org/10.47742/ijbssr.v5n4p2https://ijbssrnet.com/index.php/ijbssr We measured music listening habits, music playing and creating habits, and music preferences with relevant items by Vanstone et al. (2016).The selection and modification of the items for these variables were made through consultation with a music expert.For example, we selected "I listen to music while I perform chores or boring tasks" for the music-listening habit, "I play a musical instrument for pleasure" and "I create my music" for the music-playing and creating habit.To increase the content validity, we added "such as classical, rock, and/or country" to the original item and "I show specific preferences for certain types of music" for musical preference.This scale is rated on a 5-point Likert scale that ranges from 1 "Not at all" to "Very much".The internal consistency reliability coefficient of the scale is 0.92.
As mentioned previously, we also measured the social desirability bias with the short form of the Marlowe-Crowne social desirability scale (Reynolds, 1982).The scale has 13 items including statements such as "It is sometimes hard for me to go on with my work if I am not encouraged".Each item is rated as "True" or "False".The "True" is assigned one point and the "False" is assigned zero points.A high total score of the 13 items indicates a social desirability response tendency.The internal reliability of the scale is 0.76.

Data Analyses
We first examined the means, standard deviations, and Pearson correlations as the preliminary analyses for the main variables previously in our study.Then we conducted a partial least squares (PLS) estimation with SmartPLS 4.0.to test both the measurement model and structural model by Ringle et al. (2022).In terms of the measurement model, we initially treated all the constructs with reflective indicators and then examined the composite reliability (internal consistency), convergent validity, and discriminant validity of the reflective model.As displayed in our research model in Figure 1, (1) music playing and creating habits and (2) music listening habits are the independent variables, and smartphone addiction is the dependent variable in the structural model.Besides, self-control serves as the mediator of the associations between them, and music preference is the moderator of the association between music listening habits and selfcontrol.To evaluate the structural model, we used coefficients of determination (R2) and significance of path coefficients, while statistically controlling for the impact of age, gender, and social desirability on both self-control and smartphone addiction.

Assessment of Measurement Model
As mentioned above, we assessed the psychometric quality of the measures with three criteria: (1) composite reliability, (2) convergent validity, and (3) discriminant validity.When the composite reliability value is greater than 0.70, a measure is considered internally reliable (Nunnally and Bernstein 1994).Table 1 shows that all the values of the composite reliability exceed 0.70, which indicates that all the constructs were reliable.When the AVE for each construct exceeds 0.50 and when indicator items have higher loadings on their focal construct and lower ones on the alternative constructs within the model, convergent validity is supported (Chin, 1998;Gefen & Straub, 2005).As displayed in Table 1, after the removal of some items (i.e., items 3 and 6 of self-control), all the AVE values in the model were equal to or greater than 0.50, which indicated that each construct accounted for the majority of the variance in its items.Table 2 also shows that each of the measurement items loaded significantly higher on their focal construct than on other constructs.Based on these results, the convergent validity is evident.When a construct has its square root of the AVE that exceeds the inter-construct correlations, it is discriminant valid (Chin, 1998;Fornell & Larcker, 1981).As indicated in Table 1, the square root of the AVE of each construct exceeded the correlations between the focal construct and other constructs.With the above results, our measurement model concurs the reliability, convergent validity, and discriminant validity.Besides, the variance inflation factors (VIF) between variables range from 1.028 to 1.071, which indicates no collinearity.DOI: http://dx.doi.org/10.47742/ijbssr.v5n4p2https://ijbssrnet.com/index.php/ijbssr

Assessment of Structural Model
In our study, we conducted a structural model that was estimated based on 290 responses and 5000 bootstrapping (Chin, 2010).To evaluate the structural models, we used the size and significance of the path coefficient, coefficient of determination (R2), f2, and q2 (Götz et al., 2010).As displayed in Figure 2, Model 1 explores the mediating effect of self-control on the relationships between musical habits and smartphone addiction, and the moderating effect of music preference on the relationship between music listening habits and self-control.In the model, age, gender, and social desirability were statistically controlled.

Discussions
This current study investigates the relationships between musical habits and smartphone addiction, with a primary focus on the mediating effect of self-control.We drew on the dual-systems theories and empirical results of habit and self-control to examine how two types of musical habits (i.e., music listening and music playing and creating habits) would influence college students' smartphone addiction.Our study reveals that musical habits do not directly predict college students' smartphone addiction.Gender also does not appear to be related to smartphone addiction.The study also finds that music listening habits interact with music preference, which is positively associated with self-control.Our results also show that our respondents did not consider themselves to have significant music-playing and creating habits; this habit is found to be negatively correlated with self-control.The study finally shows that self-control negatively predicts college students' smartphone addiction.

Theoretical Contributions
This study extends the technology adoption research in three ways.First, this study extends the understanding of the antecedents of technology addiction, an emerging research stream.So far, prior research has focused on how technology use habits affect technology addiction (e.g., Turel and Qahri-Saremi, 2016).Our study advances the current literature by suggesting that musical habits, an increasingly popular practice, can indirectly influence technology addiction.
Second, our study supports the validity of dual-systems theories (Evans, 2008).Specifically, our results are consistent with dual-systems theories by revealing that musical habits directly or indirectly affected self-control, which then influenced smartphone addiction.According to the literature on implicit goal models of habits, the directional impact of habits on self-control depends on whether the habit generates positive or negative effects (Wood and Rünger, 2016).The results in our study support prior literature by showing that music playing and creating habits were negatively associated with self-control because they usually induce negative effects.It also shows that the interaction between musical listening habits and musical preference was positively associated with self-control because they generally generate positive effects.Particularly, it implies that as musical preference increases, the relationship between music-listening habits and self-control becomes stronger, regardless of whether the musiclistening habit is good or bad.
Third, the current study provides a roadmap to advance the music-IT interdisciplinary research.For music scholars, most of their research has focused on how music itself, or music interventions, change people's affect (e.g., Roy et al., 2016;Swaminathan and Schellenberg, 2015).Our study complements the music literature by indicating that music can also have an impact on self-control and smartphone addiction.These findings offer an opportunity for IS and music scholars to collaborate on using IT or designing IT artifacts to enhance people's positive affect and self-control when they listen to or play and create music.IS and music scholars can also investigate under what situations musical habits can directly prevent or reduce smartphone addiction.

International Journal of Business and Social Science Research
Vol: 5, Issue: 4 March/2024 DOI: http://dx.doi.org/10.47742/ijbssr.v5n4p2https://ijbssrnet.com/index.php/ijbssrFinally, they can also explore how to use IT to foster the formation of good musical habits.

Practical Implications
Our findings show that musical habits are associated with smartphone addiction when they increase or decrease self-control.As more college students rely on smartphones daily, we might want to look at the potential benefits of using music in combination with IT and other treatments to treat or reduce smartphone addiction.For college students who like to listen to music, we should guide them to choose songs that induce positive emotions and educate them on how to appreciate songs that express negative emotions.For example, smartphone apps can be used to increase their music literature which affects their choice and appreciation of the songs.For those who like to play and create music, we need to provide various types of interventions to improve their mood and emotions.For example, goal setting and goal striving can be used to regulate their emotion (Tamir et al. 2020).We can use goal setting or tracking apps to help them achieve the short-term goals of music playing and creating, which will promote their positive affect and self-control.

Limitations and Future Research Directions
Of course, the present study has several limitations that need to be addressed in future research.First, our study only used regular college students as its sample, which limits the generalization of its results to other populations, such as college students majoring in music.Second, our study only used a self-report survey to measure smartphone addiction.In the future, using diagnostic interviews or objective measures such as smartphone apps to measure smartphone addiction should provide more reliable findings.Third, the current study only employs a crosssectional design.Accordingly, longitudinal designs should be used to not only distinguish cause from effect but also to establish the reciprocal relationships between musical habits and smartphone addiction.For example, researchers can track how much time college students spend daily on music and examine how it is associated with the increase or decrease of smartphone addiction.Fourth, experimental research should be conducted.For instance, the impact of IT-based mindfulness interventions on self-control (e.g., Charoensukmongkol, 2016) with or without personalized music can be compared.Finally, the musical habits associated with smartphone use such as listening to streaming music and producing music with music apps are not examined in the current study.Thus, other mechanisms under which musical habits influence smartphone addiction should be explored.For example, future research should examine if people's musical habits fostered by mobile apps indirectly or directly influence smartphone addiction.Finally, the moderating effect of mobile app features on such relationships is worth investigating.

Figure 1 :
Research Model

Table 2 :
Loadings and Cross Loadings