IDEAS home Printed from https://ideas.repec.org/a/eee/ijrema/v34y2017i1p100-119.html
   My bibliography  Save this article

Modeling the role of message content and influencers in social media rebroadcasting

Author

Listed:
  • Zhang, Yuchi
  • Moe, Wendy W.
  • Schweidel, David A.

Abstract

We develop a model that examines the role of content, content-user fit, and influence on social media rebroadcasting behavior. While previous research has studied the role of content or the role of influence in the spread of social media content separately, none has simultaneously examined both in an effort to assess the relative effects of each. Our modeling approach also accounts for a message's “fit” with users, based on the content of the message and the content of messages typically shared by users.

Suggested Citation

  • Zhang, Yuchi & Moe, Wendy W. & Schweidel, David A., 2017. "Modeling the role of message content and influencers in social media rebroadcasting," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 100-119.
  • Handle: RePEc:eee:ijrema:v:34:y:2017:i:1:p:100-119
    DOI: 10.1016/j.ijresmar.2016.07.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167811616300866
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijresmar.2016.07.003?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. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    2. De Bruyn, Arnaud & Lilien, Gary L., 2008. "A multi-stage model of word-of-mouth influence through viral marketing," International Journal of Research in Marketing, Elsevier, vol. 25(3), pages 151-163.
    3. Haenlein, Michael, 2013. "Social interactions in customer churn decisions: The impact of relationship directionality," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 236-248.
    4. Teck-Hua Ho & Shan Li & So-Eun Park & Zuo-Jun Max Shen, 2012. "Customer Influence Value and Purchase Acceleration in New Product Diffusion," Marketing Science, INFORMS, vol. 31(2), pages 236-256, March.
    5. Scott K. Shriver & Harikesh S. Nair & Reto Hofstetter, 2013. "Social Ties and User-Generated Content: Evidence from an Online Social Network," Management Science, INFORMS, vol. 59(6), pages 1425-1443, June.
    6. David Bell & Sangyoung Song, 2007. "Neighborhood effects and trial on the internet: Evidence from online grocery retailing," Quantitative Marketing and Economics (QME), Springer, vol. 5(4), pages 361-400, December.
    7. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    8. East, Robert & Hammond, Kathy & Lomax, Wendy, 2008. "Measuring the impact of positive and negative word of mouth on brand purchase probability," International Journal of Research in Marketing, Elsevier, vol. 25(3), pages 215-224.
    9. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    10. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    11. Olivier Toubia & Andrew T. Stephen, 2013. "Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter?," Marketing Science, INFORMS, vol. 32(3), pages 368-392, May.
    12. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    13. Bhatia, Tulikaa & Wang, Lei, 2011. "Identifying physician peer-to-peer effects using patient movement data," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 51-61.
    14. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 441-458, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Onofrei, George & Filieri, Raffaele & Kennedy, Lorraine, 2022. "Social media interactions, purchase intention, and behavioural engagement: The mediating role of source and content factors," Journal of Business Research, Elsevier, vol. 142(C), pages 100-112.
    2. Farivar, Samira & Wang, Fang, 2022. "Effective influencer marketing: A social identity perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    3. Bo Yang & Chao Liu & Xusen Cheng & Xi Ma, 2022. "Understanding Users' Group Behavioral Decisions About Sharing Articles in Social Media: An Elaboration Likelihood Model Perspective," Group Decision and Negotiation, Springer, vol. 31(4), pages 819-842, August.
    4. Sanz-Blas, Silvia & Buzova, Daniela & Pérez-Ruiz, Pilar, 2021. "Building relational worth in an online social community through virtual structural embeddedness and relational embeddedness," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    5. Fangfang Li & Jorma Larimo & Leonidas C. Leonidou, 2021. "Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 51-70, January.
    6. Rietveld, Robert & van Dolen, Willemijn & Mazloom, Masoud & Worring, Marcel, 2020. "What You Feel, Is What You Like Influence of Message Appeals on Customer Engagement on Instagram," Journal of Interactive Marketing, Elsevier, vol. 49(C), pages 20-53.
    7. Matthew J. Schneider & Shawn Mankad, 2021. "A Two-Stage Authorship Attribution Method Using Text and Structured Data for De-Anonymizing User-Generated Content," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(3), pages 66-83, September.
    8. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    9. Hartmann, Jochen & Huppertz, Juliana & Schamp, Christina & Heitmann, Mark, 2019. "Comparing automated text classification methods," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 20-38.
    10. Irina Maiorescu & Mihaela Bucur & Bogdan Georgescu & Daniel Moise & Vasile Alecsandru Strat & Ion Daniel Zgură, 2020. "Social Media and IOT Wearables in Developing Marketing Strategies. Do SMEs Differ From Large Enterprises?," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
    11. Khan, Naseer Abbas & Khan, Ali Nawaz & Moin, Muhammad Farrukh, 2021. "Self-regulation and social media addiction: A multi-wave data analysis in China," Technology in Society, Elsevier, vol. 64(C).
    12. Bano, Shehar & Cisheng, Wu & Khan, Ali Nawaz & Khan, Naseer Abbas, 2019. "WhatsApp use and student's psychological well-being: Role of social capital and social integration," Children and Youth Services Review, Elsevier, vol. 103(C), pages 200-208.
    13. Ning Zhong & David A. Schweidel, 2020. "Capturing Changes in Social Media Content: A Multiple Latent Changepoint Topic Model," Marketing Science, INFORMS, vol. 39(4), pages 827-846, July.
    14. Qingliang Wang & Fred Miao & Giri Kumar Tayi & En Xie, 2019. "What makes online content viral? The contingent effects of hub users versus non–hub users on social media platforms," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 1005-1026, November.
    15. Tyler Horan, 2021. "Commercial Limits to Personality: Instagram Influencers and Commoditized Content Receptivity," Societies, MDPI, vol. 11(3), pages 1-10, July.
    16. Han, Zhongya & Tang, Zhongjun & He, Bo, 2022. "Improved Bass model for predicting the popularity of product information posted on microblogs," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    17. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    18. Zhao, Yan & Wen, Lingling & Feng, Xiangnan & Li, Ran & Lin, Xiaolin, 2020. "How managerial responses to online reviews affect customer satisfaction: An empirical study based on additional reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    19. Xiao Han & Leye Wang & Weiguo Fan, 2023. "Cost-Effective Social Media Influencer Marketing," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 138-157, January.
    20. Prithwiraj Mukherjee & Souvik Dutta & Arnaud De Bruyn, 2022. "Did clickbait crack the code on virality?," Journal of the Academy of Marketing Science, Springer, vol. 50(3), pages 482-502, May.
    21. Joaquin Sanchez & Carmen Abril & Michael Haenlein, 2020. "Competitive spillover elasticities of electronic word of mouth: an application to the soft drink industry," Journal of the Academy of Marketing Science, Springer, vol. 48(2), pages 270-287, March.
    22. Jia Liu & Olivier Toubia, 2018. "A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries," Marketing Science, INFORMS, vol. 37(6), pages 930-952, November.
    23. Hansen, Nele & Kupfer, Ann-Kristin & Hennig-Thurau, Thorsten, 2018. "Brand crises in the digital age: The short- and long-term effects of social media firestorms on consumers and brands," International Journal of Research in Marketing, Elsevier, vol. 35(4), pages 557-574.
    24. Lo, Pei-San & Dwivedi, Yogesh K. & Wei-Han Tan, Garry & Ooi, Keng-Boon & Cheng-Xi Aw, Eugene & Metri, Bhimaraya, 2022. "Why do consumers buy impulsively during live streaming? A deep learning-based dual-stage SEM-ANN analysis," Journal of Business Research, Elsevier, vol. 147(C), pages 325-337.

    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. Muller, Eitan & Peres, Renana, 2019. "The effect of social networks structure on innovation performance: A review and directions for research," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 3-19.
    2. Liye Ma & Ramayya Krishnan & Alan L. Montgomery, 2015. "Latent Homophily or Social Influence? An Empirical Analysis of Purchase Within a Social Network," Management Science, INFORMS, vol. 61(2), pages 454-473, February.
    3. Jing Wang & Anocha Aribarg & Yves F. Atchadé, 2013. "Modeling Choice Interdependence in a Social Network," Marketing Science, INFORMS, vol. 32(6), pages 977-997, November.
    4. Kannan, P.K. & Li, Hongshuang “Alice”, 2017. "Digital marketing: A framework, review and research agenda," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 22-45.
    5. Viswanathan, Vijay & Sese, F. Javier & Krafft, Manfred, 2017. "Social influence in the adoption of a B2B loyalty program: The role of elite status members," International Journal of Research in Marketing, Elsevier, vol. 34(4), pages 901-918.
    6. Yansong Hu & Christophe Van den Bulte, 2014. "Nonmonotonic Status Effects in New Product Adoption," Marketing Science, INFORMS, vol. 33(4), pages 509-533, July.
    7. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    8. Claus, Bart & Geyskens, Kelly & Millet, Kobe & Dewitte, Siegfried, 2012. "The referral backfire effect: The identity-threatening nature of referral failure," International Journal of Research in Marketing, Elsevier, vol. 29(4), pages 370-379.
    9. Grant Miller & A. Mushfiq Mobarak, 2015. "Learning About New Technologies Through Social Networks: Experimental Evidence on Nontraditional Stoves in Bangladesh," Marketing Science, INFORMS, vol. 34(4), pages 480-499, July.
    10. Pescher, Christian & Reichhart, Philipp & Spann, Martin, 2014. "Consumer Decision-making Processes in Mobile Viral Marketing Campaigns," Journal of Interactive Marketing, Elsevier, vol. 28(1), pages 43-54.
    11. Sebastian Schneider & Frank Huber, 2022. "You paid what!? Understanding price-related word-of-mouth and price perception among opinion leaders and innovators," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(1), pages 64-80, February.
    12. Wang, Feng & Zhang, Xueting & Chen, Man & Zeng, Wei & Cao, Rong, 2022. "The influential paradox: Brand and deal content sharing by influencers in friendship networks," Journal of Business Research, Elsevier, vol. 150(C), pages 503-514.
    13. Nejad, Mohammad G. & Amini, Mehdi & Babakus, Emin, 2015. "Success Factors in Product Seeding: The Role of Homophily," Journal of Retailing, Elsevier, vol. 91(1), pages 68-88.
    14. William Rand & Roland T. Rust & Min Kim, 2018. "Complex systems: marketing’s new frontier," AMS Review, Springer;Academy of Marketing Science, vol. 8(3), pages 111-127, December.
    15. Michael Braun & André Bonfrer, 2011. "Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes," Marketing Science, INFORMS, vol. 30(3), pages 513-531, 05-06.
    16. Landsman, Vardit & Nitzan, Irit, 2020. "Cross-decision social effects in product adoption and defection decisions," International Journal of Research in Marketing, Elsevier, vol. 37(2), pages 213-235.
    17. Sinan Aral & Dylan Walker, 2014. "Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment," Management Science, INFORMS, vol. 60(6), pages 1352-1370, June.
    18. Xiao, Yu & Han, Jingti, 2016. "Forecasting new product diffusion with agent-based models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 167-178.
    19. Yaniv Dover & Jacob Goldenberg & Daniel Shapira, 2012. "Network Traces on Penetration: Uncovering Degree Distribution from Adoption Data," Marketing Science, INFORMS, vol. 31(4), pages 689-712, July.
    20. Liu-Thompkins, Yuping & Rogerson, Michelle, 2012. "Rising to Stardom: An Empirical Investigation of the Diffusion of User-generated Content," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 71-82.

    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:eee:ijrema:v:34:y:2017:i:1:p:100-119. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-research-in-marketing/ .

    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.