39
40 Dugan, B. M., Herman, T., Hatch, L., & Bradford, A. (2011). EFFECTIVE USES OF SOCIAL MEDIA
MARKETING IN THE MUSIC INDUSTRY.
Eigenraam, A. W., Eelen, J., & Verlegh, P. W. J. (2021). Let Me Entertain You? The Importance of Authenticity in Online Customer Engagement. Journal of Interactive Marketing, 54, 53–68.
https://doi.org/10.1016/j.intmar.2020.11.001
Eurostat. (2021). Artificial intelligence in EU enterprises. Artificial Intelligence in EU Enterprises - Eurostat.
Every Noise at once. (2022). EveryNoise.
Freeman, S., Gibbs, M., & Nansen, B. (2022). ‘Don’t mess with my algorithm’: Exploring the relationship between listeners and automated curation and recommendation on music streaming services.
First Monday. https://doi.org/10.5210/fm.v27i1.11783
Fry, A. (n.d.). Beyond the black box in music streaming: the impact of recommendation systems upon artists Dr Marcus O’ Dair, Associate Dean of Knowledge Exchange and Enterprise, University of the Arts London.
Gillespie, T. (2014). The Relevance of Algorithms. In Media Technologies (Issue October).
https://doi.org/10.7551/mitpress/9780262525374.003.0009
Gordon, R. A. (1987). Social Desirability Bias: A Demonstration and Technique for Its Reduction.
Teaching of Psychology, 14(1). https://doi.org/10.1207/s15328023top1401_11
Green, T., & Sinclair, G. (2022). The broken record: How the music industry is making sense of uncertainty and change. Canadian Journal of Administrative Sciences, 39(2).
https://doi.org/10.1002/cjas.1659
Hainmueller, J. (2014). Do Survey Experiments Capture Real-World Behavior? External Validation of Conjoint and Vignette Analyses with a Natural Experiment ∗.
Hamdani, N. A., & Permana, I. (2021). Customer Loyalty: A Case Study of Spotify (Vol. 25).
http://annalsofrscb.ro
Helmholz, P., Meyer, M., & Robra-Bissantz, S. (2020). Feel the moosic: Emotion-based music selection and recommendation. 32nd Bled EConference Humanizing Technology for a Sustainable Society, BLED 2019 - Conference Proceedings, June, 203–221. https://doi.org/10.18690/978-961-286-280-0.11
HesterJuliaVoddé. (2021). Curation or Control: Analyzing the Socioeconomic Context of Recommendation Engines for Cultural Content.
Hosey, C., Vujović, L., st. Thomas, B., Garcia-Gathright, J., & Thom, J. (2019a). Just give me what I want:
How people use and evaluate music search. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3290605.3300529
Hosey, C., Vujović, L., st. Thomas, B., Garcia-Gathright, J., & Thom, J. (2019b, May 2). Just give me what I want: How people use and evaluate music search. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3290605.3300529
Hume, A., Cernuzzi, L., & Zarza, J. L. (2022). Analysis of the Big-Five personality traits in the Chatbot
“UC-Paraguay.” In CLEI electronic journal (Vol. 25, Issue 2). https://www.itu.int/en/ITU-D/Statistics/Pages/facts/default.aspx
IFPI. (2021). Engaging with music 2021.
IFPI releases Engaging with Music 2021. (2021). IFPI releases Engaging with Music 2021.
Jeremy Wade Morris. (2020). Music Platforms and the Optimization of Culture.
https://doi.org/10.1177/2056305120940690
41 Jiang, Y., Yang, X., & Zheng, T. (2022). Make chatbots more adaptive: Dual pathways linking
human-like cues and tailored response to trust in interactions with chatbots. Computers in Human Behavior, 107485. https://doi.org/10.1016/J.CHB.2022.107485
Kaitajärvi-Tiekso, J. (2021). Maria Eriksson, Rasmus Fleischer, Anna Johansson, Pelle Snickars, and Patrick Vonderau, Spotify Teardown: Inside the Black Box of Streaming Music. Transposition, 9.
https://doi.org/10.4000/transposition.5667
Kamehkhosh, I., Bonnin, G., & Jannach, D. (2020). Effects of recommendations on the playlist creation behavior of users. User Modeling and User-Adapted Interaction, 30(2), 285–322.
https://doi.org/10.1007/s11257-019-09237-4
Knijnenburg, B. P., Sivakumar, S., & Wilkinson, D. (2016). Recommender systems for self-actualization.
RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems.
https://doi.org/10.1145/2959100.2959189
Koelsch, S. (2018). Investigating the Neural Encoding of Emotion with Music. In Neuron (Vol. 98, Issue 6, pp. 1075–1079). Cell Press. https://doi.org/10.1016/j.neuron.2018.04.029
Köhler, C. F., Rohm, A. J., de Ruyter, K., & Wetzels, M. (2011). Return on interactivity: The impact of online agents on newcomer adjustment. Journal of Marketing, 75(2).
https://doi.org/10.1509/jmkg.75.2.93
Kowald, D., Schedl, M., & Lex, E. (2020). The unfairness of popularity bias in music recommendation:
A reproducibility study. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12036 LNCS, 35–42.
https://doi.org/10.1007/978-3-030-45442-5_5
Lee, J. H., Pritchard, L., & Hubbles, C. (2019). Can we listen to it together: Factors influencing reception of music recommendations and post-recommendation behavior. Proceedings of the 20th International Society for Music Information Retrieval Conference, ISMIR 2019, 663–669.
Li, Y., Zhang, Q., Li, A. R., & Cheng, J. (2021). Business Model Comparison Between Spotify and NetEase Cloud Music.
Mackenzie, A. (2017). Machine learners: Archeology of a data practice. MIT Press.
Marie Charlotte Götting. (2022a, June). Spotify - statistics & facts. Statista.
https://www.statista.com/topics/2075/spotify/
Marie Charlotte Götting. (2022b, August). Number of Spotify premium subscribers worldwide from 1st quarter 2015 to 2nd quarter of 2022. Statista.
Mehrotra, R. (2021). Algorithmic Balancing of Familiarity, Similarity, & Discovery in Music Recommendations. International Conference on Information and Knowledge Management, Proceedings, 3996–4005. https://doi.org/10.1145/3459637.3481893
Mourougan, S., & Sethuraman, Dr. K. (2017). Hypothesis Development and Testing. IOSR Journal of Business and Management, 19(05), 34–40. https://doi.org/10.9790/487x-1905013440
Muñoz-Leiva, F., Sánchez-Fernández, J., Montoro-Ríos, F., & Ibáñez-Zapata, J. Á. (2010). Improving the response rate and quality in Web-based surveys through the personalization and frequency of reminder mailings. Quality and Quantity, 44(5). https://doi.org/10.1007/s11135-009-9256-5 Nair, A., Pillai, S., Nair, G. S., & Anjali, T. (2021). Emotion Based Music Playlist Recommendation System
using Interactive Chatbot. Proceedings of the 6th International Conference on Communication
and Electronics Systems, ICCES 2021, 1767–1772.
https://doi.org/10.1109/ICCES51350.2021.9489138 NLTK. (2022). NLTK. NLTK Project.
42 Nowak, K. L., & Rauh, C. (2005). The influence of the avatar on online perceptions of anthropomorphism, androgyny, credibility, homophily, and attraction. Journal of Computer-Mediated Communication, 11(1). https://doi.org/10.1111/j.1083-6101.2006.tb00308.x
Panda, R., & Paiva, R. P. (2021). HOW DOES THE SPOTIFY API COMPARE TO THE MUSIC EMOTION RECOGNITION STATE-OF-THE-ART? https://research.atspotify.com/
Pandas. (2022). Pandas.
Patrik N. Juslin. (2019). Musical Emotions Explained: Unlocking the Secrets of Musical Affect (Oxford).
Ploty. (2022). Ploty.
Prey, R. (2018). Nothing personal: algorithmic individuation on music streaming platforms. Media, Culture and Society, 40(7), 1086–1100. https://doi.org/10.1177/0163443717745147
Priya Dialani. (2021). TECH FOR ENJOYING MUSIC – HERE’S HOW SPOTIFY USES BIG DATA.
Reshmi, S., & Balakrishnan, K. (2016). Implementation of an inquisitive chatbot for database supported knowledge bases. Sadhana - Academy Proceedings in Engineering Sciences, 41(10).
https://doi.org/10.1007/s12046-016-0544-1
Rita, A., & Quintino, P. (n.d.). Title: The impact of chatbot technology attributes on customer experience: an example in telecom.
Salles, A., Evers, K., & Farisco, M. (2020). Anthropomorphism in AI. AJOB Neuroscience, 11(2), 88–95.
https://doi.org/10.1080/21507740.2020.1740350
Sánchez-Moreno, D., Zheng, Y., & Moreno-García, M. N. (2020). Time-aware music recommender systems: Modeling the evolution of implicit user preferences and user listening habits in a collaborative filtering approach. Applied Sciences (Switzerland), 10(15).
https://doi.org/10.3390/APP10155324
Schedl, M., Zamani, H., Chen, C. W., Deldjoo, Y., & Elahi, M. (2018). Current challenges and visions in music recommender systems research. International Journal of Multimedia Information Retrieval, 7(2), 95–116. https://doi.org/10.1007/s13735-018-0154-2
Serban, C. (2010). Marketing Communication in Online Social Programs Ohanian Model of Source Credibility. Annals of the University of Oradea: Economic Science, 1(1).
Sharma, J., Sharma, K., Garg, K., & Sharma, A. K. (2021). Product recommendation system a comprehensive review. IOP Conference Series: Materials Science and Engineering, 1022(1).
https://doi.org/10.1088/1757-899X/1022/1/012021
Shaw, D., McMaster, R., & Newholm, T. (2016). Care and Commitment in Ethical Consumption: An Exploration of the ‘Attitude–Behaviour Gap.’ Journal of Business Ethics, 136(2).
https://doi.org/10.1007/s10551-014-2442-y
Sherga Jr, R. M., Wei, D., Benson, N., Javed, F., Jr, R. M., & Sherga, M. (2021). Alternative Methods for Deriving Emotion Metrics in the Spotify® Recommendation Algorithm. In SMU Data Science
Review (Vol. 5, Issue 3).
https://scholar.smu.edu/datasciencereviewAvailableat:https://scholar.smu.edu/datasciencerev iew/vol5/iss3/3http://digitalrepository.smu.edu.
Siles, I., Segura-Castillo, A., Sancho, M., & Solís-Quesada, R. (2019). Genres as Social Affect: Cultivating Moods and Emotions through Playlists on Spotify. Social Media and Society, 5(2).
https://doi.org/10.1177/2056305119847514
Song, Y., Dixon, S., & Pearce, M. T. (2012). A survey of music recommendation systems and future perspectives. Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval (CMMR), June 2012, 395–410.
43 Statista Research Department. (2015, November). Number of monthly active users of selected mobile messaging apps in 2015 and 2020. Statista. https://www.statista.com/statistics/487943/mobile-messaging-apps-monthly-active-users/
Striphas, T. (2015). Algorithmic culture. European Journal of Cultural Studies, 18(5), 395–412.
https://doi.org/10.1177/1367549415577392
van Dolen, W. M., Dabholkar, P. A., & de Ruyter, K. (2007). Satisfaction with Online Commercial Group Chat: The Influence of Perceived Technology Attributes, Chat Group Characteristics, and Advisor Communication Style. Journal of Retailing, 83(3). https://doi.org/10.1016/j.jretai.2007.03.004 Werner, A. (2020). Organizing music, organizing gender: algorithmic culture and Spotify
recommendations. Popular Communication, 18(1), 78–90.
https://doi.org/10.1080/15405702.2020.1715980
Winkler, R., & Soellner, M. (2018). Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. Academy of Management Proceedings, 2018(1).
https://doi.org/10.5465/ambpp.2018.15903abstract
Wishwanath, C. H. P. D., & Ahangama, S. (2019, September 1). A Personalized Music Recommendation System based on User Moods. 19th International Conference on Advances in ICT for Emerging Regions, ICTer 2019 - Proceedings. https://doi.org/10.1109/ICTer48817.2019.9023727
Yong Chai, J., Kit Khen Ken, L., Him Chan, K., Xuan Wan, S., & Tin Ting, T. (2021). DIGITAL MUSIC: A STUDY OF FACTORS IN INFLUENCING ONLINE MUSIC STREAMING SERVICE PURCHASE (Vol. 168).
Yousefian Jazi, S., Kaedi, M., & Fatemi, A. (2021a). An emotion-aware music recommender system:
bridging the user’s interaction and music recommendation. Multimedia Tools and Applications, 80(9), 13559–13574. https://doi.org/10.1007/s11042-020-10386-7
Yousefian Jazi, S., Kaedi, M., & Fatemi, A. (2021b). An emotion-aware music recommender system:
bridging the user’s interaction and music recommendation. Multimedia Tools and Applications, 80(9), 13559–13574. https://doi.org/10.1007/s11042-020-10386-7
Zarouali, B., van den Broeck, E., Walrave, M., & Poels, K. (2018). Predicting Consumer Responses to a Chatbot on Facebook. Cyberpsychology, Behavior, and Social Networking, 21(8).
https://doi.org/10.1089/cyber.2017.0518
Zentner, M., Grandjean, D., & Scherer, K. R. (2008). Emotions Evoked by the Sound of Music:
Characterization, Classification, and Measurement. Emotion, 8(4), 494–521.
https://doi.org/10.1037/1528-3542.8.4.494
Zhou, C., Jin, Y., Zhang, K., Yuan, J., Li, S., & Wang, X. (2018). MusicRoBot: Towards conversational context-aware music recommender system. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10828 LNCS, 817–820. https://doi.org/10.1007/978-3-319-91458-9_55
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