Skip navigation

DSpace

機構典藏 DSpace 系統致力於保存各式數位資料(如:文字、圖片、PDF)並使其易於取用。

點此認識 DSpace
DSpace logo
English
中文
  • 瀏覽論文
    • 校院系所
    • 出版年
    • 作者
    • 標題
    • 關鍵字
    • 指導教授
  • 搜尋 TDR
  • 授權 Q&A
    • 我的頁面
    • 接受 E-mail 通知
    • 編輯個人資料
  1. NTU Theses and Dissertations Repository
  2. 文學院
  3. 圖書資訊學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/103739
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor唐牧群zh_TW
dc.contributor.advisorMuh-Chyun Tangen
dc.contributor.author鄭雨桐zh_TW
dc.contributor.authorYu-Tong Zhengen
dc.date.accessioned2026-08-19T16:22:58Z-
dc.date.available2026-08-20-
dc.date.copyright2026-08-19-
dc.date.issued2026-
dc.date.submitted2026-08-11 16:33:47-
dc.identifier.citationAcker, M., & McReynolds, P. (1967). The “Need for Novelty”: A Comparison of Six Instruments. The Psychological Record, 17(2), 177-182. https://doi.org/10.1007/BF03393702
Ackermann, T.-I., & Merrill, J. (2022). Rationales and functions of disliked music: An in-depth interview study. PLOS ONE, 17(2), e0263384. https://doi.org/10.1371/journal.pone.0263384
Anderson, A., Maystre, L., Anderson, I., Mehrotra, R., & Lalmas, M. (2020). Algorithmic Effects on the Diversity of Consumption on Spotify Proceedings of The Web Conference 2020, Taipei, Taiwan. https://doi.org/10.1145/3366423.3380281
Bainbridge, D., Cunningham, S. J., & Downie, J. S. (2003). How People Describe Their Music Information Needs: A Grounded Theory Analysis Of Music Queries. Paper presented at 4th International Symposium on Music Information Retrieval, ISMIR 2003, Baltimore, United States. https://jscholarship.library.jhu.edu/items/e4037d9f-8861-4b23-913c-c7dce2de3401
Baird, A., & Samson, S. (2014). Music evoked autobiographical memory after severe acquired brain injury: Preliminary findings from a case series. Neuropsychological Rehabilitation, 24(1), 125-143. https://doi.org/10.1080/09602011.2013.858642
Bernard, H. R., Killworth, P., Kronenfeld, D., & Sailer, L. (1984). The problem of informant accuracy: The validity of retrospective data. Annual Review of Anthropology, 13, 495–517.
Bonneville-Roussy, A., Rentfrow, P. J., Xu, M. K., & Potter, J. (2013). Music through the ages: Trends in musical engagement and preferences from adolescence through middle adulthood [Article]. Journal of Personality and Social Psychology, 105(4), 703-717. https://doi.org/10.1037/a0033770
Bornstein, R. F. (1989). EXPOSURE AND AFFECT - OVERVIEW AND META-ANALYSIS OF RESEARCH, 1968-1987. Psychological Bulletin, 106(2), 265-289. https://doi.org/10.1037/0033-2909.106.2.265
Briggs, S. R., & Cheek, J. M. (1986). The role of factor analysis in the development and evaluation of personality scales. Journal of Personality, 54(1), 106-148. https://doi.org/https://doi.org/10.1111/j.1467-6494.1986.tb00391.x
Brown, J. J., & Reingen, P. H. (1987). Social Ties and Word-of-Mouth Referral Behavior*. Journal of Consumer Research, 14(3), 350-362. https://doi.org/10.1086/209118
Brown, S. (2006). The Perpetual Music Track: The Phenomenon of Constant Musical Imagery. Journal of Consciousness Studies, 13(6), 43-62. https://www.ingentaconnect.com/content/imp/jcs/2006/00000013/00000006/art00003
Burkart, P. (2008). Trends in Digital Music Archiving. The Information Society, 24(4), 246-250. https://doi.org/10.1080/01972240802191621
Chakravarty, A., Liu, Y., & Mazumdar, T. (2010). The Differential Effects of Online Word-of-Mouth and Critics’ Reviews on Pre-release Movie Evaluation. Journal of Interactive Marketing, 24(3), 185-197. https://doi.org/10.1016/j.intmar.2010.04.001
Chamorro-Premuzic, T., & Furnham, A. (2007). Personality and music: Can traits explain how people use music in everyday life? British Journal of Psychology, 98(2), 175-185. https://doi.org/https://doi.org/10.1348/000712606X111177
Chamorro-Premuzic, T., Swami, V., Furnham, A., & Maakip, I. (2009). The big five personality traits and uses of music: A replication in Malaysia using structural equation modeling. Journal of Individual Differences, 30(1), 20-27. https://doi.org/10.1027/1614-0001.30.1.20
Chang, Y.-H., & Tang, M.-C. (2018). Serendipity with Music Streaming Services: The Mediating Role of User and Task Characteristics. Transforming Digital Worlds, Cham.
Cheney-Lippold, J. (2011). A New Algorithmic Identity:Soft Biopolitics and the Modulation of Control. Theory, Culture & Society, 28(6), 164-181. https://doi.org/10.1177/0263276411424420
Chevalier, J. A., & Mayzlin, D. (2006). The Effect of Word of Mouth on Sales: Online Book Reviews. Journal of Marketing Research, 43(3), 345-354. https://doi.org/10.1509/jmkr.43.3.345
Cole, S. (2025). A song for each moment: Identifying listening modes as reflexive practices in music streaming. Big Data & Society, 12(2), 20539517251338742. https://doi.org/10.1177/20539517251338742
Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the experience-sampling method. Journal of Nervous and Mental Disease, 175(9), 526-536. https://doi.org/10.1097/00005053-198709000-00004
Csikszentmihalyi, M., Larson, R., & Prescott, S. (1977). The ecology of adolescent activity and experience. Journal of youth and adolescence, 6(3), 281-294.
Cunningham, S. J., Bainbridge, D., & McKay, D. (2007). Finding new music: a diary study of everyday encounters with novel songs (Version 1). Swinburne. https://doi.org/10.25916/sut.26275825.v1
Datta, H., Knox, G., & Bronnenberg, B. J. (2018). Changing Their Tune: How Consumers’ Adoption of Online Streaming Affects Music Consumption and Discovery. Marketing Science, 37(1), 5-21. https://doi.org/10.1287/mksc.2017.1051
DeNora, T. (1999). Music as a technology of the self. Poetics, 27(1), 31-56. https://doi.org/https://doi.org/10.1016/S0304-422X(99)00017-0
DeNora, T. (2006). Music and self-identity. In A. Bennett, B. Shank, & J. Toynbee (Eds.), The popular music studies reader (pp. 141–147). London: Routledge.
Erdelez, S. (1997). Information encountering: a conceptual framework for accidental information discovery Proceedings of an international conference on Information seeking in context, Tampere, Finland.
Erdelez, S. (1999). Information Encountering: It's More Than Just Bumping into Information. Bulletin of the American Society for Information Science and Technology, 25(3), 26-29. https://doi.org/https://doi.org/10.1002/bult.118
Eriksson, M., Fleischer, R., Johansson, A., Snickars, P., & Vonderau, P. (2019). Spotify teardown: Inside the black box of streaming music. Mit Press.
Ferwerda, B., & Graus, M. (2018). Predicting musical sophistication from music listening behaviors: A preliminary study. arXiv preprint, arXiv:1808.07314.
Ferwerda, B., & Tkalčič, M. (2019). Exploring Online Music Listening Behaviors of Musically Sophisticated Users Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, Larnaca, Cyprus. https://doi.org/10.1145/3314183.3324974
Ferwerda, B., Graus, M. P., Vall, A., Tkalcic, M., & Schedl, M. (2017). How item discovery enabled by diversity leads to increased recommendation list attractiveness. In 32nd Annual ACM Symposium on Applied Computing, SAC 2017 (Vol. Part F128005, pp. 1693-1696). Association for Computing Machinery, Inc.. https://doi.org/10.1145/3019612.3019899
Ferwerda, B., Yang, E., Schedl, M., & Tkalcic, M. (2019). Personality and taxonomy preferences, and the influence of category choice on the user experience for music streaming services. Multimedia Tools and Applications, 78(14), 20157-20190. https://doi.org/10.1007/s11042-019-7336-7
Floridou, G. A., & Müllensiefen, D. (2015). Environmental and mental conditions predicting the experience of involuntary musical imagery: An experience sampling method study. Consciousness and Cognition, 33, 472-486. https://doi.org/https://doi.org/10.1016/j.concog.2015.02.012
Gagne, J. P. (2024). What My Music Says About Me: Re-evaluating the ‘Badge’ Function of Music in the Context of Streaming. YOUNG, 32(3), 330-345. https://doi.org/10.1177/11033088231218854
Gardikiotis, A., & Baltzis, A. (2012). ‘Rock music for myself and justice to the world!’: Musical identity, values, and music preferences. Psychology of Music, 40(2), 143-163. https://doi.org/10.1177/0305735610386836
Gillebaart, M., Förster, J., & Rotteveel, M. (2012). Mere exposure revisited: the influence of growth versus security cues on evaluations of novel and familiar stimuli. J Exp Psychol Gen, 141(4), 699-714. https://doi.org/10.1037/a0027612
Greasley, A. E., & Lamont, A. (2011). Exploring engagement with music in everyday life using experience sampling methodology. Musicae Scientiae, 15(1), 45-71. https://doi.org/10.1177/1029864910393417
Hagen, A. N. (2015). The Playlist Experience: Personal Playlists in Music Streaming Services. Popular Music and Society, 38(5), 625-645. https://doi.org/10.1080/03007766.2015.1021174
Hesmondhalgh, D. (2008). Towards a critical understanding of music, emotion and self‐identity. Consumption Markets & Culture, 11(4), 329-343. https://doi.org/10.1080/10253860802391334
Hracs, B. J., & Webster, J. (2021). From selling songs to engineering experiences: exploring the competitive strategies of music streaming platforms. Journal of Cultural Economy, 14(2), 240-257. https://doi.org/10.1080/17530350.2020.1819374
Janata, P., Tomic, S. T., & Rakowski, S. K. (2007). Characterisation of music-evoked autobiographical memories. Memory, 15(8), 845-860. https://doi.org/10.1080/09658210701734593
Jannach, D., Resnick, P., Tuzhilin, A., & Zanker, M. (2016). Recommender systems — beyond matrix completion. Commun. ACM, 59(11), 94–102. https://doi.org/10.1145/2891406
Jin, Y., Tintarev, N., & Verbert, K. (2018). Effects of personal characteristics on music recommender systems with different levels of controllability. In Recsys 2018 12th ACM Conference on Recommender Systems (pp. 13–21). https://doi.org/10.1145/3240323.3240358
John, O. P., Srivastava, S., Pervin, L. A., & John, O. P. (1999). The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research (2nd ed.).
Juslin, P. N. (2019). Musical Emotions Explained: Unlocking the Secrets of Musical Affect. Oxford University Press. https://doi.org/10.1093/oso/9780198753421.001.0001
Juslin, P. N., & Sloboda, J. A. (2001). Music And Emotion: Theory and research. Oxford University Press. https://doi.org/10.1093/oso/9780192631886.001.0001
Juslin, P. N., & Västfjäll, D. (2008). Emotional responses to music: The need to consider underlying mechanisms. Behavioral and Brain Sciences, 31(5), 559-575. https://doi.org/10.1017/S0140525X08005293
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4), 441-504. https://doi.org/10.1007/s11257-011-9118-4
Komiak, S. Y. X., & Benbasat, I. (2006). The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents. MIS Quarterly, 30(4), 941-960. https://doi.org/10.2307/25148760
Krause, A. E., Dimmock, J., Rebar, A. L., & Jackson, B. (2021). Music listening predicted improved life satisfaction in university students during early stages of the COVID-19 pandemic. Frontiers in Psychology, 11, Article 631033. https://doi.org/10.3389/fpsyg.2020.631033
Kvavilashvili, L., & Mandler, G. (2004). Out of one’s mind: A study of involuntary semantic memories. Cognitive Psychology, 48(1), 47-94. https://doi.org/https://doi.org/10.1016/S0010-0285(03)00115-4
Kyle, G., Absher, J., Norman, W., Hammitt, W., & Jodice, L. (2007). A Modified Involvement Scale. Leisure Studies - LEIS STUD, 26, 399-427. https://doi.org/10.1080/02614360600896668
Lee, J. H., & Price, R. (2015). Understanding Users of Commercial Music Services through Personas: Design Implications. International Society for Music Information Retrieval Conference.
Lee, J. H., & Waterman, N. M. (2012). Understanding User Requirements for Music Information Services. International Society for Music Information Retrieval Conference.
Lee, J. H., & Waterman, N. M. (2012). Understanding User Requirements for Music Information Services. International Society for Music Information Retrieval Conference.
Lee, M., & Kim, H.-J. (2023). A collaborative filtering model incorporating media promotions and users' variety-seeking tendencies in the digital music market. Decision Support Systems, 174, 114022. https://doi.org/https://doi.org/10.1016/j.dss.2023.114022
Li, J., Lin, H.-R., Wolf, A., & Lothwesen, K. (2024). Measuring musical sophistication in the Chinese general population: Validation and replication of the Simplified Chinese Gold-MSI. Musicae Scientiae, 28(2), 197-221. https://doi.org/10.1177/10298649231183264
Liang, Y., & Willemsen, M. C. (2023). Promoting Music Exploration through Personalized Nudging in a Genre Exploration Recommender. International Journal of Human–Computer Interaction, 39(7), 1495-1518. https://doi.org/10.1080/10447318.2022.2108060
Liang, Y., & Willemsen, M. C. (2023). Promoting music exploration through personalized nudging in a genre exploration recommender. International Journal of Human–Computer Interaction, 39(7), 1495–1518. https://doi.org/10.1080/10447318.2022.2108060
Liikkanen, L. A. (2012). Musical activities predispose to involuntary musical imagery. Psychology of Music, 40(2), 236-256. https://doi.org/10.1177/0305735611406578
Lonsdale, A. J., & North, A. C. (2011). Why do we listen to music? A uses and gratifications analysis. British Journal of Psychology, 102(1), 108-134. https://doi.org/https://doi.org/10.1348/000712610X506831
Lüders, M. (2019). Pushing music: People’s continued will to archive versus Spotify’s will to make them explore. European Journal of Cultural Studies, 24(4), 952-969. https://doi.org/10.1177/1367549419862943
MacDonald, R., & Saarikallio, S. (2022). Musical identities in action: Embodied, situated, and dynamic. Musicae Scientiae, 26(4), 729-745. https://doi.org/10.1177/10298649221108305
MacDonald, R., Miell, D., & Hargreaves, D. J. (2002). Musical Identities. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199298457.013.0043
Mäntymäki, M., & Islam, A. K. M. N. (2015). Gratifications from using freemium music streaming services: Differences between basic and premium users. International Conference on Interaction Sciences.
Maw, W. H., & Maw, E. W. (1962). Selection of unbalanced and unusual designs by children high in curiosity. Child Dev, 33, 917-922. https://doi.org/10.1111/j.1467-8624.1962.tb05127.x
McCourt, T. (2005). Collecting Music in the Digital Realm. Popular Music and Society, 28(2), 249-252. https://doi.org/10.1080/03007760500045394
McCourt, T., & Zuberi, N. (2016). Music and discovery. Popular Communication, 14(3), 123-126. https://doi.org/10.1080/15405702.2016.1199025
McCrae, R. R., & John, O. P. (1992). An introduction to the five-factor model and its applications. Journal of Personality, 60(2), 175-215. https://doi.org/10.1111/j.1467-6494.1992.tb00970.x
McIntyre, N. (1989). The Personal Meaning of Participation: Enduring Involvement. Journal of Leisure Research, 21(2), 167–179. https://doi.org/10.1080/00222216.1989.11969797
Mckenzie, A. (2017). Machines Learners: Archaeology of a Data Practice. The MIT Press. https://mitpress.mit.edu/9780262537865/machine-learners/
McReynolds, P. (1962). Exploratory Behavior: A Theoretical Interpretation. Psychological Reports, 11(2), 311-318. https://doi.org/10.2466/pr0.1962.11.2.311
Mischel, W. (1968). Personality and assessment. John Wiley & Sons Inc.
Mittal, B. (1995). A comparative analysis of four scales of consumer involvement. Psychology & Marketing, 12(7), 663-682. https://doi.org/https://doi.org/10.1002/mar.4220120708
Miwa, M., Egusa, Y., Saito, H., Takaku, M., Terai, H., & Kando, N. (2011). A method to capture information encountering embedded in exploratory Web searches. Information Research, 16(3), 16-13.
Morris, J. W. (2015a). Curation by code: Infomediaries and the data mining of taste. European Journal of Cultural Studies, 18(4-5), 446-463. https://doi.org/10.1177/1367549415577387
Morris, J. W. (2015b). Selling digital music, formatting culture. University of California Press.
Morris, J. W., & Powers, D. (2015). Control, curation and musical experience in streaming music services. Creative Industries Journal, 8(2), 106-122. https://doi.org/10.1080/17510694.2015.1090222
Müllensiefen, D., Gingras, B., Musil, J., & Stewart, L. (2014). The Musicality of Non-Musicians: An Index for Assessing Musical Sophistication in the General Population. PLOS ONE, 9(2), e89642. https://doi.org/10.1371/journal.pone.0089642
Murakami, T., Mori, K., & Orihara, R. (2008). Metrics for Evaluating the Serendipity of Recommendation Lists. In K. Satoh, A. Inokuchi, K. Nagao, & T. Kawamura, New Frontiers in Artificial Intelligence Berlin, Heidelberg.
Parisi, L. (2013). Contagious Architecture: Computation, Aesthetics, and Space. The MIT Press.
Prey, R. (2018). Nothing personal: algorithmic individuation on music streaming platforms. Media, Culture & Society, 40(7), 1086-1100. https://doi.org/10.1177/0163443717745147
Prior, M. (2005). News vs. Entertainment: How Increasing Media Choice Widens Gaps in Political Knowledge and Turnout. American Journal of Political Science, 49(3), 577-592. https://doi.org/https://doi.org/10.1111/j.1540-5907.2005.00143.x
Raff, A., Mladenow, A., & Strauss, C. (2021). Music Discovery as Differentiation Strategy for Streaming Providers Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services, Chiang Mai, Thailand. https://doi.org/10.1145/3428757.3429151
Randall, W. M., & Rickard, N. S. (2017). Personal music listening: A model of emotional outcomes developed through mobile experience sampling. Music Perception, 34(5), 501-514. https://doi.org/10.1525/MP.2017.34.5.501
Rawlings, D., & Ciancarelli, V. (1997). Music Preference and the Five-Factor Model of the NEO Personality Inventory. Psychology of Music - PSYCHOL MUSIC, 25, 120-132. https://doi.org/10.1177/0305735697252003
Rentfrow, P. J., & Gosling, S. D. (2003). The do re mi's of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236-1256. https://doi.org/10.1037/0022-3514.84.6.1236
Russell, C. A., & Levy, S. J. (2011). The Temporal and Focal Dynamics of Volitional Reconsumption: A Phenomenological Investigation of Repeated Hedonic Experiences. Journal of Consumer Research, 39(2), 341-359. https://doi.org/10.1086/662996
Schäfer, T., Sedlmeier, P., Städtler, C., & Huron, D. (2013). The psychological functions of music listening [Original Research]. Frontiers in Psychology, Volume 4 - 2013. https://doi.org/10.3389/fpsyg.2013.00511
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
Seabrook, J. 2014. “Spotify: Friend or Foe?” The New Yorker, November 24. Accessed 17 July 2025. http://www.newyorker.com/magazine/2014/11/24/revenue-streams
Seaver, N. (2019). Captivating algorithms: Recommender systems as traps. Journal of Material Culture, 24(4), 421-436. https://doi.org/10.1177/1359183518820366
Sheldrick Ross, C. (1999). Finding without seeking: the information encounter in the context of reading for pleasure. Information Processing & Management, 35(6), 783-799. https://doi.org/https://doi.org/10.1016/S0306-4573(99)00026-6
Silvia, P. (2011). On Personality and Piloerection: Individual Differences in Aesthetic Chills and Other Unusual Aesthetic Experiences. Psychology of Aesthetics, Creativity, and the Arts, 5, 208-214. https://doi.org/10.1037/a0021914
Silvia, P., Fayn, K., & Beaty, R. (2015). Openness to Experience and Awe in Response to Nature and Music: Personality and Profound Aesthetic Experiences. Psychology of Aesthetics, Creativity, and the Arts, 9, 376-384. https://doi.org/10.1037/aca0000028
Simonson, I. (2005). Determinants of Customers’ Responses to Customized Offers: Conceptual Framework and Research Propositions. Journal of Marketing, 69(1), 32-45. https://doi.org/10.1509/jmkg.69.1.32.55512
Sloan, P. 2013. “The Future of Music, According to Spotify’s Daniel Ek.” CNET, April 9. Accessed 21 July 2025. http://www.cnet.com/news/the-future-of-music-according-to-spotifys-daniel-ek/
Sloboda, J. A., O’Neill, S. A., & Ivaldi, A. (2001). Functions of Music in Everyday Life: An Exploratory Study Using the Experience Sampling Method. Musicae Scientiae, 5(1), 9-32.
Snickars, P. (2017). More of the same - On spotify radio [Article]. Culture Unbound, 9(2), 184-211. https://doi.org/10.3384/cu.2000.1525.1792184
Tang, M.-C., & Jhang, P.-S. (2020). Music discovery and revisiting behaviors of individuals with different preference characteristics: An experience sampling approach. Journal of the Association for Information Science and Technology, 71(5), 540-552. https://doi.org/https://doi.org/10.1002/asi.24259
Tang, M.-C., & Liao, I.-H. (2022). Preference diversity and openness to novelty: Scales construction from the perspective of movie recommendation. Journal of the Association for Information Science and Technology, 73(9), 1222-1235. https://doi.org/https://doi.org/10.1002/asi.24628
Tang, M.-C., & Yang, M.-Y. (2017). Evaluating Music Discovery Tools on Spotify: The Role of User Preference Characteristics. Journal of Library & Information Studies, 15(1). https://jlis.lis.ntu.edu.tw/files/journal/j44-1.pdf
Tang, M.-C., Chang, M.-M., & Lin, S.-C. (2018). The development and validation of “preference diversity” and “openness to novelty” scales for movie goers. Proceedings of the Association for Information Science and Technology, 55(1), 486-493. https://doi.org/https://doi.org/10.1002/pra2.2018.14505501053
Tang, M.-C., Sie, Y.-J., & Ting, P.-H. (2014). Evaluating books finding tools on social media: A case study of aNobii. Information Processing & Management, 50(1), 54-68. https://doi.org/https://doi.org/10.1016/j.ipm.2013.07.005
Terry, P. C., Karageorghis, C. I., Curran, M. L., Martin, O. V., & Parsons-Smith, R. L. (2020). Effects of music in exercise and sport: A meta-analytic review. Psychological Bulletin, 146(2), 91-117. https://doi.org/10.1037/bul0000216
Thompson, W. F., Bullot, N. J., & Margulis, E. H. (2023). The psychological basis of music appreciation: Structure, self, source. Psychological Review, 130(1), 260-284. https://doi.org/10.1037/rev0000364
Tkalcic, M., & Chen, L. (2015). Personality and Recommender Systems. Recommender Systems Handbook.
Tsukuda, K., & Goto, M. (2020). Explainable Recommendation for Repeat Consumption Proceedings of the 14th ACM Conference on Recommender Systems, Virtual Event, Brazil. https://doi.org/10.1145/3383313.3412230
Vuoskoski, J. K., & Eerola, T. (2011). Measuring music-induced emotion: A comparison of emotion models, personality biases, and intensity of experiences. Musicae Scientiae, 15(2), 159-173. https://doi.org/10.1177/1029864911403367
Ward, M. K., Goodman, J. K., & Irwin, J. R. (2014). The same old song: The power of familiarity in music choice. Marketing Letters, 25(1), 1-11. https://doi.org/10.1007/s11002-013-9238-1
Watson, D., & Mandryk, R. L. (2012). An in-situ study of real-life listening context. In Proceedings of the 9th Sound and Music Computing Conference (pp. 11-16).
Webster, J. (2019). Taste in the platform age: music streaming services and new forms of class distinction. Information, Communication & Society, 23(13), 1909-1924. https://doi.org/10.1080/1369118X.2019.1622763
Webster, J. (2023). The promise of personalisation: Exploring how music streaming platforms are shaping the performance of class identities and distinction. New Media & Society, 25(8), 2140-2162. https://doi.org/10.1177/14614448211027863
Wellman, J. D., Roggenbuck, J. W., & Smith, A. C. (1982). Recreation specialization and norms of depreciative behavior among canoesists. Journal of Leisure Research, 14(4), 323-340. https://doi.org/10.1080/00222216.1982.11969529
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
Wikstrom, P. 2009. The Music Industry: Music in the Cloud. Cambridge: Polity Press, Digital Media and Society.
Williamson, V. J., Jilka, S. R., Fry, J., Finkel, S., Müllensiefen, D., & Stewart, L. (2012). How do "earworms" start? Classifying the everyday circumstances of Involuntary Musical Imagery [Article]. Psychology of Music, 40(3), 259-284. https://doi.org/10.1177/0305735611418553
Yarmey, A. D. (1979). The Psychology of Eyewitness Testimony. Free Press.
Zaichkowsky, J. L. (1985). Measuring the Involvement Construct*. Journal of Consumer Research, 12(3), 341-352. https://doi.org/10.1086/208520
Zhong, C., Shah, S., Sundaravadivelan, K., & Sastry, N. (2013). Sharing the loves: Understanding the how and why of online content curation. In Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013 (pp. 659-667). AAAI Press. http://www.inf.kcl.ac.uk/staff/nrs/projects/cd-gain/icwsm13.html
-
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/103739-
dc.description.abstract近年來,音樂串流服務已成為人們接觸音樂的主要途徑,其中Spotify結合演算法推薦、個人播放清單、收藏功能與主動搜尋等多元功能,讓使用者得以在主動探索與被動推薦之間進行不同程度的音樂發現。現有研究多聚焦於平台演算法或使用紀錄分析,較少同時考量個人的音樂心理特質及真實生活情境中音樂聆聽的觸發因素如何共同影響音樂發現與聆聽行為。本研究以Spotify為研究平台,將使用者之平台功能使用、日常音樂聆聽情境與個人音樂心理特質加以整合,探討使用者如何在不同音樂心理特質下展現不同的音樂發現模式與平台功能使用行為,並進一步分析其對聆聽歷程之影響。
本研究採用問卷調查與經驗抽樣法(Experience Sampling Method, ESM)之兩階段混合研究設計。第一階段以問卷蒐集使用者之音樂聆聽行為習慣資料並量測音樂心理特質,包括Gold-MSI兩構面(情感投入與積極參與)、音樂涉入四構面(吸引力/中心性、身分認同、自我探索、社會連結),並新發展出音樂開放性類型(選擇性開放、普遍開放、熟悉導向、背景陪伴),以衡量使用者於熟悉與新奇音樂之間的探索傾向。第二階段以兩週的經驗抽樣法蒐集了57位參與者日常生活中的即時音樂聆聽資料,記錄其聆聽觸發因素、Spotify平台功能使用情形、聆聽歷程中新曲目比例及聆聽享受程度,並結合因素分析、羅吉斯迴歸模型、線性混合模型與結構方程模型等方法進行分析,以全面探討音樂心理特質、平台功能使用與音樂發現行為之間的關係。
研究結果顯示,使用者之音樂心理特質與平台功能使用存在顯著差異。不同心理特質會影響使用者偏好的Spotify功能類型、觸發有機聆聽的內外部提示來源,以及新曲目探索程度。其中,社會連結是影響有機聆聽觸發因素的重要心理特質構面;自我探索與積極參與程度較高者的新音樂探索更多來自主動搜尋與平台外資訊來源,不僅僅依賴演算法推薦。聆聽享受程度方面,有機聆聽最能提升聆聽享受,但不同特質構面存在不同的最佳功能適配。開放性類型在解釋音樂發現行為上具有關鍵角色,普遍開放者較傾向接受演算法推薦並接觸新音樂。結構方程模型進一步發現,Gold-MSI與音樂涉入對平台功能使用的影響,部分透過開放性類型產生直接或間接效果,顯示開放性類型在音樂心理特質與平台使用行為之間具有重要的中介角色。此外,研究亦發現,使用者的音樂心理特質不僅影響其於Spotify平台內的功能選擇,也影響平台外部由情緒、記憶、社交互動或環境刺激所引發的音樂探索歷程。
整體而言,本研究從三項音樂心理特質、平台功能使用與真實生活情境層面建構完整的音樂發現歷程分析架構,補充過去僅依賴平台行為資料而難以解釋使用者內在動機之不足,並提出「開放性類型」作為理解音樂發現差異的重要心理構念。研究結果顯示,音樂發現並非僅受演算法設計所驅動,而是平台功能、個人心理特質與日常生活情境交互作用的結果。此研究除擴展音樂資訊行為與音樂心理學之理論發展外,亦可作為音樂串流平台未來發展心理特質專屬推薦機制、音樂探索功能設計及個人化使用體驗之參考依據。
zh_TW
dc.description.abstractIn recent years, music streaming services have become the primary channel through which people access music. Spotify, in particular, integrates a range of features—including algorithmic recommendations, user-created playlists, music libraries, and active search functions—that enable users to discover music through varying degrees of active exploration and passive recommendation. Existing studies have primarily focused on recommendation algorithms or platform usage data, while relatively little attention has been paid to how individual musical psychological dispositions and real-life listening contexts jointly shape music discovery and listening behaviors. Using Spotify as the research platform, this study integrates users' platform feature usage, everyday music listening contexts, and individual musical psychological dispositions to investigate how different psychological characteristics influence music discovery patterns and platform feature use, as well as their subsequent effects on the listening experience.
This study adopted a two-phase mixed-method design combining a questionnaire survey with the Experience Sampling Method (ESM). In the first phase, participants completed a questionnaire measuring their music listening habits and musical psychological dispositions, including two dimensions of the Goldsmiths Musical Sophistication Index (Gold-MSI) — Emotional Engagement and Active Engagement — and four dimensions of music involvement: Attraction/Centrality, Identity Expression, Self-Affirmation, and Social Bonding. In addition, this study developed a new construct, Openness Styles, consisting of Selective Openness, General Openness, Familiarity Preference, and Background Listening, to assess individuals' tendencies toward balancing familiarity and musical novelty. In the second phase, two weeks of ESM data were collected from 57 participants to capture their real-time music listening experiences in daily life, including listening cues, Spotify feature usage, the proportion of newly discovered songs during each listening session, and listening enjoyment. The data were analyzed using factor analysis, logistic regression, linear mixed-effects models, and structural equation modeling to comprehensively examine the relationships among musical psychological dispositions, platform feature usage, and music discovery behaviors.
The results revealed significant associations between users' musical psychological dispositions and their platform feature usage. Different psychological dispositions influenced users' preferences for Spotify features, the internal and external cues that initiated organic listening, and the extent of music exploration. Among the psychological constructs examined, Openness Styles played a pivotal role in explaining music discovery behaviors. Individuals characterized by General Openness were more likely to embrace algorithmic recommendations and explore unfamiliar music. Social Bonding emerged as an important psychological dimension influencing listening cues, whereas individuals with higher levels of Self-Affirmation and Active Engagement tended to discover new music through active searching and external information sources beyond the platform, rather than relying solely on algorithmic recommendations. Regarding listening enjoyment, organic listening produced the highest levels of enjoyment, although the optimal platform feature varied across different psychological profiles. Structural equation modeling further demonstrated that the effects of Gold-MSI and music involvement on platform feature usage were partially mediated by Openness Styles, highlighting its important mediating role between musical psychological dispositions and platform usage behaviors. Furthermore, musical psychological dispositions influenced not only users' feature choices within Spotify but also music discovery processes initiated by emotions, memories, social interactions, and environmental stimuli outside the platform.
Beyond contributing to the theoretical development of music information behavior and music psychology, this study may also provide a reference for music streaming platforms in developing psychologically tailored recommendation mechanisms, designing music exploration features, and enhancing personalized user experiences.
en
dc.description.provenanceSubmitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-08-19T16:22:58Z
No. of bitstreams: 0
en
dc.description.provenanceMade available in DSpace on 2026-08-19T16:22:58Z (GMT). No. of bitstreams: 0en
dc.description.tableofcontents誌 謝 i
摘 要 ii
Abstract iv
目 次 vi
圖 次 viii
表 次 x
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究問題 7
第三節 名詞解釋 8
第四節 研究範圍與限制 12
第二章 文獻回顧 13
第一節 音樂串流平台與使用行為 13
第二節 心理特質與音樂發現 22
第三節 聆聽動機在聆聽行為中的角色 37
第三章 研究設計與實施 43
第一節 研究設計 44
第二節 研究流程 54
第三節 資料蒐集與分析 58
第四章 研究結果與分析 63
第一節 音樂心理特質問卷資料分析 63
第二節 經驗抽樣問卷資料分析 85
第三節 綜合分析與討論 99
第四節 研究問題回應 134
第五章 結論與建議 141
第一節 結論 141
第二節 研究限制與建議 144
參考文獻 149
附錄一 音樂心理特質問卷 159
附錄二 音樂聆聽行為經驗抽樣問卷 165
附錄三 研究知情同意書 169
-
dc.language.isozh_TW-
dc.subject經驗抽樣法-
dc.subject音樂聆聽行為-
dc.subject音樂資訊行為-
dc.subject音樂心理特質-
dc.subject演算法推薦-
dc.subjectExperience Sampling Method (ESM)-
dc.subjectmusic listening behavior-
dc.subjectmusic information behavior-
dc.subjectmusical psychological traits-
dc.subjectalgorithmic recommendation-
dc.title以經驗抽樣法探索不同心理特質Spotify使用者之音樂聆聽行為zh_TW
dc.titleExploring Music Listening Behaviors of Spotify Users with Different Psychological Traits: An Experience Sampling Approachen
dc.typeThesis-
dc.date.schoolyear114-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee蔡天怡;蕭宗銘zh_TW
dc.contributor.oralexamcommitteeTien-I Tsai;Tsung-Ming Hsiaoen
dc.subject.keyword經驗抽樣法; 音樂聆聽行為; 音樂資訊行為; 音樂心理特質; 演算法推薦zh_TW
dc.subject.keywordExperience Sampling Method (ESM); music listening behavior; music information behavior; musical psychological traits; algorithmic recommendationen
dc.relation.page170-
dc.identifier.doi10.6342/NTU202603749-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2026-08-14-
dc.contributor.author-college文學院-
dc.contributor.author-dept圖書資訊學系-
dc.date.embargo-lift2031-08-07-
顯示於系所單位:圖書資訊學系

文件中的檔案:
檔案 大小格式 
ntu-114-2.pdf
  未授權公開取用
4.52 MBAdobe PDF檢視/開啟
顯示文件簡單紀錄


系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。

社群連結
聯絡資訊
10617臺北市大安區羅斯福路四段1號
No.1 Sec.4, Roosevelt Rd., Taipei, Taiwan, R.O.C. 106
Tel: (02)33662353
Email: ntuetds@ntu.edu.tw
意見箱
相關連結
館藏目錄
國內圖書館整合查詢 MetaCat
臺大學術典藏 NTU Scholars
臺大圖書館數位典藏館
本站聲明
© NTU Library All Rights Reserved