Analysis Factors of Intention to Use Music as a Service Application: A Case Study of Spotify Application

Chandra, Yakob Utama; Christian, Lay; Juwitasary, Hanny; Atmojo, Robertus Nugroho Perwiro; Febrianto, William

doi:10.1109/ic3ina.2018.8629521

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Meta-analyses have shown that the most common extensions of the TAM are the "subjective norm" and the "attitude toward using" from the TRA, with the latter typically yielding more meaningful results (Schepers and Wetzels, 2007;Avci Yucel and Gulbahar, 2013). The TAM and the TRA as well as their variations have been successfully applied to MSS (Wagner and Hess, 2013;Hampton-Sosa, 2017;Chen et al, 2018;Danckwerts et al, 2018;Chandra et al, 2019;Henning and Ruth, 2020).…”

Section: State Of Researchmentioning

confidence: 99%

Modulation of Negative Affect Predicts Acceptance of Music Streaming Services, While Personality Does Not

Hilsdorf

Bullerjahn

2021

Front. Psychol.

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Music streaming services (MSS) offer their users numerous ways of choosing and implementing their individual approaches to music listening. Personality, uses of music, and the acceptance of MSS can be conceptualized as interdependent. This study investigates whether negative affect modulation strategies explain differences in the acceptance of MSS and integrates findings from previous research into a structural equation model. As for measurements, the Big Five Inventory 2, the Inventory for the Assessment of Activation and Arousal modulation through Music, and adapted scales from previous research on the Technology Acceptance Model were used. A convenience sample of 825 participants (24.3 years; 74% females and 89% students) successfully completed an online questionnaire. In total, 89 percent of the sample reported using MSS regularly. The results show that the tendency to modulate negative affect through music is positively influenced by openness and neuroticism. In turn, the tendency to modulate negative affect through music is shown to increase the perceived usefulness of MSS. However, this study failed to replicate the previous findings that openness increases the attitude toward using and that neuroticism decreases the perceived usefulness. This implies that uses of music are more effective than personality traits at predicting the individual acceptance of MSS. However, personality can be viewed as a predictor for uses of music. The interwovenness of stable and situational factors of music choices is supported. MSS seem to assist their users in coping with negative affect in everyday life, increasing wellbeing. MSS should expand their personalization features to optimize user experience with respect to individual uses of music.

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Section: State Of Researchmentioning

confidence: 99%

Modulation of Negative Affect Predicts Acceptance of Music Streaming Services, While Personality Does Not

Hilsdorf

Bullerjahn

2021

Front. Psychol.

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“…Initial models of technology acceptance were developed to predict acceptance of workplace technologies (Davis, 1989). The unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al, 2003) and later extensions (Venkatesh et al, 2012) have expanded its applicability from organizational contexts to consumer-technologies, including streaming services (Chandra et al, 2018;Hampton-Sosa, 2017). Others have used elements of the model to predict intention to use music streaming services (Bolduc & Kinnally, 2018), or to predict usage rates of streaming apps (Oyedele & Simpson, 2018).…”

Section: Theoretical Framework and Hypothesesmentioning

confidence: 99%

“…Music streaming services offer more than affordable, effortless and convenient access to large libraries of music: they provide features for organizing music and attempt to offer accurate recommendations. The importance of these features has not been examined in UTAUT or related types of studies (Bolduc & Kinnally, 2018;Chandra et al, 2018;Hampton-Sosa, 2017;Oyedele & Simpson, 2018). The theoretical reasoning behind the potential importance of these features will be addressed next.…”

Section: Theoretical Framework and Hypothesesmentioning

confidence: 99%

Ubiquitous tunes, virtuous archiving and catering for algorithms: the tethered affairs of people and music streaming services

Lüders

2020

Information, Communication & Society

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Music streaming services all provide affordable and easy access to massive databases of music and instead attempt to increase customer loyalty by optimizing personalized recommendations and offering opportunities for listeners to build their 'own' music libraries. In this paper, these features are operationalized as the convenience, price, archive and algorithmic value of streaming services. Existing studies detail the multitude of ways of making sense of these services, but do not allow for specifying what features matter more. Drawing on an online survey and interviews, this study indicates that price, convenience and archive value predict continued intention to use music streaming services. Survey results negate the importance of algorithmic value, but the interviews suggest that for some, algorithmic individuation is too evasive to be noticed. Those who 'see' and value recommendations 'cater for algorithms' and consider their own listening and archiving practices as input for optimizing output. Combined, the different features set the stage for both overt and covert tethering of people and streaming services.

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Song and Artist Attributes Analysis For Spotify

Grace¹

2022

2022 International Conference on Engineering and Emerging Technologies (ICEET)

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Analysis Factors of Intention to Use Music as a Service Application: A Case Study of Spotify Application

Cited by 3 publications

References 11 publications

Modulation of Negative Affect Predicts Acceptance of Music Streaming Services, While Personality Does Not

Modulation of Negative Affect Predicts Acceptance of Music Streaming Services, While Personality Does Not

Ubiquitous tunes, virtuous archiving and catering for algorithms: the tethered affairs of people and music streaming services

Song and Artist Attributes Analysis For Spotify

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