Factors influencing continuous intention to use online music streaming platforms: the mediating role of personalization

Abstract

The topic discusses factors influencing the intention to continue using online music platforms, along with identifying the role and impact on intention through personalization. The research aims to: (i) identify factors influencing the intention to continue using online music platforms, (ii) measure the extent of influence of these factors on the intention to use music platforms, (iii) analyze the impact of personalization variables on the intention to continue using, and clarify the mediating role of personalization on the relationship between other variables and the intention to continue using, and (iv) based on the level of influence of the factors, provide businesses with timely adaptation strategies amidst intense competition in the online music platform sector. The research was conducted in two phases: secondary data research and quantitative research. Through secondary data research, an initial model was proposed consisting of 6 factors influencing Continuous Use Intention (CI), including Artificial Intelligence (AI), Online Trust (OT), Social Influence (SI), Habit (H), Performance Expectancy (PE), Effort Expectancy (EE), along with considering the influence of the intermediate factor Personalization (P). Subsequently, using the quantitative research method with a sample size of 329, the collected data was processed to evaluate, and measure the influence, and impact on Continuous Use Intention (CI). The results of the study indicated that: Artificial Intelligence (AI), Online Trust (OT), Social Influence (SI), and Performance Expectancy (PE) do not have a direct impact on Continuous Use Intention (CI). However, Habit (H) and Effort Expectancy (EE) have an impact on Continuous Use Intention (CI). Additionally, the results showed that Personalization (P) does not mediate the relationship between Artificial Intelligence (AI), Social Influence (SI), Habit (H), and Continuous Use Intention (CI). However, Personalization (P) is qualified to mediate the relationship between Online Trust (OT), Performance Expectancy (PE), Effort Expectancy (EE), and Continuous Use Intention (CI). Through the analysis results, we made recommendations for online music platforms such as improving the quality of user interface and features. The improvement process must align with user trends and needs, making them feel interested, convenient, and comfortable to use. Additionally, we propose integrating secondary data research, quantitative research, and qualitative research to increase objectivity for future studies.