IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v11y2024i1d10.1057_s41599-024-03636-8.html
   My bibliography  Save this article

Yesterday once more: collective storytelling and public engagement with digital cultural products on the music streaming platform

Author

Listed:
  • Cheng-Jun Wang

    (Nanjing University)

  • Xinzhi Zhang

    (City University of Hong Kong)

  • Zepeng Gou

    (Nanjing University)

  • Youqin Wu

    (Nanjing University)

Abstract

Drawing on narrative transportation theory, we propose that when people consume a cultural product, they consume their emotions and memories through collective storytelling. Such emotions and memories are amplified by user comments on social media, enhancing the product’s influence and triggering audience engagement. We collected public data from NetEase Cloud Music—a major music streaming platform in mainland China—to investigate how the emotions and memories expressed in user comments influence the songs’ impact. Findings indicate that autobiographical narratives and negative emotions in user comments significantly boost a song’s influence. Moreover, user comments are particularly effective in promoting emerging artists with limited resources compared to their more established counterparts. This study advances the narrative transportation theory by emphasizing the role of active audiences and collective storytelling. Empirically, it extends the existing literature on the factors influencing cultural products on music streaming platforms in a non-Western context.

Suggested Citation

  • Cheng-Jun Wang & Xinzhi Zhang & Zepeng Gou & Youqin Wu, 2024. "Yesterday once more: collective storytelling and public engagement with digital cultural products on the music streaming platform," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
  • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03636-8
    DOI: 10.1057/s41599-024-03636-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-024-03636-8
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-024-03636-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kim, Jae-Eun & Lloyd, Stephen & Cervellon, Marie-Cécile, 2016. "Narrative-transportation storylines in luxury brand advertising: Motivating consumer engagement," Journal of Business Research, Elsevier, vol. 69(1), pages 304-313.
    2. Appel, Markus, 2022. "Affective resistance to narrative persuasion," Journal of Business Research, Elsevier, vol. 149(C), pages 850-859.
    3. Wu, Bo & Shen, Haiying, 2015. "Analyzing and predicting news popularity on Twitter," International Journal of Information Management, Elsevier, vol. 35(6), pages 702-711.
    4. Tom van Laer & Ko de Ruyter & Luca M. Visconti & Martin Wetzels, 2014. "The Extended Transportation-Imagery Model: A Meta-Analysis of the Antecedents and Consequences of Consumers' Narrative Transportation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 40(5), pages 797-817.
    5. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
    6. Holbrook, Morris B & Batra, Rajeev, 1987. "Assessing the Role of Emotions as Mediators of Consumer Responses to Advertising," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(3), pages 404-420, December.
    7. Sean J. Taylor & Lev Muchnik & Madhav Kumar & Sinan Aral, 2023. "Identity effects in social media," Nature Human Behaviour, Nature, vol. 7(1), pages 27-37, January.
    8. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
    9. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Karolina Kaczorowska & Jodie Conduit & Steven Goodman, 2024. "Engaging through storytelling: the interplay of engagement with a story, cause, and charity," Journal of Brand Management, Palgrave Macmillan, vol. 31(3), pages 265-292, May.
    2. Dessart, Laurence & Pitardi, Valentina, 2019. "How stories generate consumer engagement: An exploratory study," Journal of Business Research, Elsevier, vol. 104(C), pages 183-195.
    3. Antioco, Michael & Coussement, Kristof & Fletcher-Chen, Chavi Chi-Yun & Prange, Christiane, 2023. "What's in a word? Adopting a linguistic-style analysis of western MNCs’ global press releases," Journal of World Business, Elsevier, vol. 58(2).
    4. Agrawal, Shiv Ratan & Mittal, Divya, 2022. "Optimizing customer engagement content strategy in retail and E-tail: Available on online product review videos," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    5. van Laer, Tom & Feiereisen, Stephanie & Visconti, Luca M., 2019. "Storytelling in the digital era: A meta-analysis of relevant moderators of the narrative transportation effect," Journal of Business Research, Elsevier, vol. 96(C), pages 135-146.
    6. Martin Haselmayer & Marcelo Jenny, 2017. "Sentiment analysis of political communication: combining a dictionary approach with crowdcoding," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2623-2646, November.
    7. Young Bin Kim & Sang Hyeok Lee & Shin Jin Kang & Myung Jin Choi & Jung Lee & Chang Hun Kim, 2015. "Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    8. Singh, Amit & Jenamani, Mamata & Thakkar, Jitesh J. & Rana, Nripendra P., 2022. "Quantifying the effect of eWOM embedded consumer perceptions on sales: An integrated aspect-level sentiment analysis and panel data modeling approach," Journal of Business Research, Elsevier, vol. 138(C), pages 52-64.
    9. Ping-Yu Hsu & Hong-Tsuen Lei & Shih-Hsiang Huang & Teng Hao Liao & Yao-Chung Lo & Chin-Chun Lo, 2019. "Effects of sentiment on recommendations in social network," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 253-262, June.
    10. Fatma Najar & Nizar Bouguila, 2023. "On smoothing and scaling language model for sentiment based information retrieval," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 725-744, September.
    11. Luis J. Callarisa-Fiol & Miguel Ángel Moliner-Tena & Rosa Rodríguez-Artola & Javier Sánchez-García, 2023. "Entrepreneurship innovation using social robots in tourism: a social listening study," Review of Managerial Science, Springer, vol. 17(8), pages 2945-2971, November.
    12. Phuong Nguyen Hong Huynh & Tin Trung Hoang & Huynh Thi Thuy Phan & Quynh Le Nhu Nguyen, 2024. "The customers’ perception of privacy in the retail industry when adopting digital transformation," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 14(2), pages 143-159.
    13. Jifeng Mu & Jonathan Z. Zhang, 2021. "Seller marketing capability, brand reputation, and consumer journeys on e-commerce platforms," Journal of the Academy of Marketing Science, Springer, vol. 49(5), pages 994-1020, September.
    14. Annamalai, Balamurugan & Yoshida, Masayuki & Varshney, Sanjeev & Pathak, Atul Arun & Venugopal, Pingali, 2021. "Social media content strategy for sport clubs to drive fan engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    15. P. D. Mahendhiran & S. Kannimuthu, 2018. "Deep Learning Techniques for Polarity Classification in Multimodal Sentiment Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 883-910, May.
    16. Simon Albrecht & Bernhard Lutz & Dirk Neumann, 2020. "The behavior of blockchain ventures on Twitter as a determinant for funding success," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(2), pages 241-257, June.
    17. Yankang Su & Zbigniew J. Kabala, 2023. "Public Perception of ChatGPT and Transfer Learning for Tweets Sentiment Analysis Using Wolfram Mathematica," Data, MDPI, vol. 8(12), pages 1-27, November.
    18. Yawar Abbas & M. S. I. Malik, 2023. "Defective products identification framework using online reviews," Electronic Commerce Research, Springer, vol. 23(2), pages 899-920, June.
    19. Li, Xinwei & Xu, Mao & Zeng, Wenjuan & Tse, Ying Kei & Chan, Hing Kai, 2023. "Exploring customer concerns on service quality under the COVID-19 crisis: A social media analytics study from the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    20. Razzaq, Ali & Shao, Wei & Quach, Sara, 2024. "Meme marketing effectiveness: A moderated-mediation model," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03636-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.