IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v311y2023i1p316-332.html
   My bibliography  Save this article

Mining Twitter lists to extract brand-related associative information for celebrity endorsement

Author

Listed:
  • Saridakis, Charalampos
  • Katsikeas, Constantine S.
  • Angelidou, Sofia
  • Oikonomidou, Maria
  • Pratikakis, Polyvios

Abstract

Twitter lists (i.e., curated collections of Twitter accounts) are user-generated and serve primarily as a tool to group other users. Grouping judgments are grounded in the implicit assumption that co-listed members share common associations. As such, Twitter lists are ideal for directly exploring associative links between brands and/or other entities. This research capitalizes on Twitter list membership data to provide a new metric indicating the similarity of users’ list membership profiles. This metric is used as a proxy for perceptions of brand–celebrity (mis)fit (i.e., the degree of congruency or similarity between the celebrity and the brand) in celebrity endorsement situations, where a celebrity's fame or social status is used to promote a brand. To validate the accuracy of the method, we compare the list similarity metric with directly elicited survey data for a test set of 62 celebrities and 64 brands, ranging across eight industry sectors. This research contributes to the extant literature of studies extracting brand-related associative information (i.e., information held in consumers’ memory that contains the meaning of a brand) from large volumes of consumer online data. This research also introduces new ways of data mining to operational research literature and provides managers with a new methodology to directly infer perceptions of brand–celebrity (mis)fit.

Suggested Citation

  • Saridakis, Charalampos & Katsikeas, Constantine S. & Angelidou, Sofia & Oikonomidou, Maria & Pratikakis, Polyvios, 2023. "Mining Twitter lists to extract brand-related associative information for celebrity endorsement," European Journal of Operational Research, Elsevier, vol. 311(1), pages 316-332.
  • Handle: RePEc:eee:ejores:v:311:y:2023:i:1:p:316-332
    DOI: 10.1016/j.ejor.2023.05.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037722172300348X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2023.05.004?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. Sharad Goel & Daniel G. Goldstein, 2014. "Predicting Individual Behavior with Social Networks," Marketing Science, INFORMS, vol. 33(1), pages 82-93, January.
    2. Elizabeth Duthie & Diogo Veríssimo & Aidan Keane & Andrew T Knight, 2017. "The effectiveness of celebrities in conservation marketing," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-16, July.
    3. Albert, Noël & Ambroise, Laure & Valette-Florence, Pierre, 2017. "Consumer, brand, celebrity: Which congruency produces effective celebrity endorsements?," Journal of Business Research, Elsevier, vol. 81(C), pages 96-106.
    4. Li, Han & Gupta, Ashish & Zhang, Jie & Flor, Nick, 2020. "Who will use augmented reality? An integrated approach based on text analytics and field survey," European Journal of Operational Research, Elsevier, vol. 281(3), pages 502-516.
    5. Kahr, Michael & Leitner, Markus & Ruthmair, Mario & Sinnl, Markus, 2021. "Benders decomposition for competitive influence maximization in (social) networks," Omega, Elsevier, vol. 100(C).
    6. Maheswaran, Durairaj & Sternthal, Brian, 1990. "The Effects of Knowledge, Motivation, and Type of Message on Ad Processing and Product Judgments," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(1), pages 66-73, June.
    7. Christopher R. Knittel & Victor Stango, 2014. "Celebrity Endorsements, Firm Value, and Reputation Risk: Evidence from the Tiger Woods Scandal," Management Science, INFORMS, vol. 60(1), pages 21-37, January.
    8. Johannes Knoll & Jörg Matthes, 2017. "The effectiveness of celebrity endorsements: a meta-analysis," Journal of the Academy of Marketing Science, Springer, vol. 45(1), pages 55-75, January.
    9. B Baesens & C Mues & D Martens & J Vanthienen, 2009. "50 years of data mining and OR: upcoming trends and challenges," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 16-23, May.
    10. N. Albert & Laure Ambroise & Pierre Valette-Florence, 2017. "Consumer, brand, celebrity: Which congruency produces effective celebrity endorsements?. 81, 96-106," Post-Print hal-02127678, HAL.
    11. Anna Makrides & Demetris Vrontis & Michael Christofi, 2020. "The Gold Rush of Digital Marketing: Assessing Prospects of Building Brand Awareness Overseas," Business Perspectives and Research, , vol. 8(1), pages 4-20, January.
    12. Jonah Berger & Alan T. Sorensen & Scott J. Rasmussen, 2010. "Positive Effects of Negative Publicity: When Negative Reviews Increase Sales," Marketing Science, INFORMS, vol. 29(5), pages 815-827, 09-10.
    13. Smith, Scott M. & Roster, Catherine A. & Golden, Linda L. & Albaum, Gerald S., 2016. "A multi-group analysis of online survey respondent data quality: Comparing a regular USA consumer panel to MTurk samples," Journal of Business Research, Elsevier, vol. 69(8), pages 3139-3148.
    14. Daniel M. Ringel & Bernd Skiera, 2016. "Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data," Marketing Science, INFORMS, vol. 35(3), pages 511-534, May.
    15. Aron Culotta & Jennifer Cutler, 2016. "Mining Brand Perceptions from Twitter Social Networks," Marketing Science, INFORMS, vol. 35(3), pages 343-362, May.
    16. Buzeta, Cristian & De Pelsmacker, Patrick & Dens, Nathalie, 2020. "Motivations to Use Different Social Media Types and Their Impact on Consumers' Online Brand-Related Activities (COBRAs)," Journal of Interactive Marketing, Elsevier, vol. 52(C), pages 79-98.
    17. Henderson, Geraldine R. & Iacobucci, Dawn & Calder, Bobby J., 1998. "Brand diagnostics: Mapping branding effects using consumer associative networks," European Journal of Operational Research, Elsevier, vol. 111(2), pages 306-327, December.
    18. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    19. Torres, Anna & Bijmolt, Tammo H.A., 2009. "Assessing brand image through communalities and asymmetries in brand-to-attribute and attribute-to-brand associations," European Journal of Operational Research, Elsevier, vol. 195(2), pages 628-640, June.
    20. Adjei Peter Darko & Decui Liang, 2023. "A heterogeneous opinion-driven decision-support model for tourists’ selection with different travel needs in online reviews," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 74(1), pages 272-289, January.
    21. Long Mai & Bac Le, 2021. "Joint sentence and aspect-level sentiment analysis of product comments," Annals of Operations Research, Springer, vol. 300(2), pages 493-513, May.
    22. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    23. Dimitris Bertsimas & Arthur Delarue & Patrick Jaillet & Sébastien Martin, 2019. "Travel Time Estimation in the Age of Big Data," Operations Research, INFORMS, vol. 67(2), pages 498-515, March.
    24. Gregan-Paxton, Jennifer & John, Deborah Roedder, 1997. "Consumer Learning by Analogy: A Model of Internal Knowledge Transfer," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(3), pages 266-284, December.
    25. 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.
    26. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    27. Glen L. Urban & Philip L. Johnson & John R. Hauser, 1984. "Testing Competitive Market Structures," Marketing Science, INFORMS, vol. 3(2), pages 83-112.
    28. Noël Albert & Laure Ambroise & Pierre Valette-Florence, 2017. "Consumer, brand, celebrity: Which congruency produces effective celebrity endorsements?," Post-Print hal-01998102, HAL.
    29. Symitsi, Efthymia & Stamolampros, Panagiotis & Daskalakis, George & Korfiatis, Nikolaos, 2021. "The informational value of employee online reviews," European Journal of Operational Research, Elsevier, vol. 288(2), pages 605-619.
    30. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    31. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
    32. Park, C Whan & Milberg, Sandra & Lawson, Robert, 1991. "Evaluation of Brand Extensions: The Role of Product Feature Similarity and Brand Concept Consistency," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(2), pages 185-193, September.
    33. Misra, Shekhar & Beatty, Sharon E., 1990. "Celebrity spokesperson and brand congruence : An assessment of recall and affect," Journal of Business Research, Elsevier, vol. 21(2), pages 159-173, September.
    34. Dinesh Puranam & Vishal Narayan & Vrinda Kadiyali, 2017. "The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors," Marketing Science, INFORMS, vol. 36(5), pages 726-746, September.
    35. Yu, Shubin & Hu, Yangjuan, 2020. "When luxury brands meet China: The effect of localized celebrity endorsements in social media marketing," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    36. Belanche, Daniel & Casaló, Luis V. & Flavián, Marta & Ibáñez-Sánchez, Sergio, 2021. "Understanding influencer marketing: The role of congruence between influencers, products and consumers," Journal of Business Research, Elsevier, vol. 132(C), pages 186-195.
    37. Scholz, Michael & Pfeiffer, Jella & Rothlauf, Franz, 2017. "Using PageRank for non-personalized default rankings in dynamic markets," European Journal of Operational Research, Elsevier, vol. 260(1), pages 388-401.
    38. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2021. "Tensorial graph learning for link prediction in generalized heterogeneous networks," European Journal of Operational Research, Elsevier, vol. 290(1), pages 219-234.
    39. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2019. "Individual-level social influence identification in social media: A learning-simulation coordinated method," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1005-1015.
    40. Dolnicar, Sara & Rossiter, John R., 2008. "The low stability of brand-attribute associations is partly due to market research methodology," International Journal of Research in Marketing, Elsevier, vol. 25(2), pages 104-108.
    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. Maximilian Matthe & Daniel M. Ringel & Bernd Skiera, 2023. "Mapping Market Structure Evolution," Marketing Science, INFORMS, vol. 42(3), pages 589-613, May.
    2. Ishita Chakraborty & Minkyung Kim & K. Sudhir, 2019. "Attribute Sentiment Scoring With Online Text Reviews : Accounting for Language Structure and Attribute Self-Selection," Cowles Foundation Discussion Papers 2176R2, Cowles Foundation for Research in Economics, Yale University, revised Jun 2021.
    3. Alzate, Miriam & Arce-Urriza, Marta & Cebollada, Javier, 2022. "Mining the text of online consumer reviews to analyze brand image and brand positioning," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    4. Peng Li & Yang Sun, 2024. "Impact of internet celebrities’ short videos on audiences’ visit intentions: Is beauty power?," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    5. Boukis, Achilleas, 2023. "Storytelling in initial coin offerings: Attracting investment or gaining referrals?," Journal of Business Research, Elsevier, vol. 160(C).
    6. Zhu, Xiajing & Teng, Lefa & Foti, Lianne & Yuan, Yige, 2019. "Using self-congruence theory to explain the interaction effects of brand type and celebrity type on consumer attitude formation," Journal of Business Research, Elsevier, vol. 103(C), pages 301-309.
    7. Ishita Chakraborty & Minkyung Kim & K. Sudhir, 2019. "Attribute Sentiment Scoring With Online Text Reviews : Accounting for Language Structure and Attribute Self-Selection," Cowles Foundation Discussion Papers 2176, Cowles Foundation for Research in Economics, Yale University.
    8. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    9. Xuhui Wang & Asad Hassan Butt & Qilin Zhang & Nouman Shafique & Hassan Ahmad, 2021. "“Celebrity Avatar†Feasting on In-Game Items: A Gamers’ Play Arena," SAGE Open, , vol. 11(2), pages 21582440211, May.
    10. Venciute, Dominyka & Mackeviciene, Ieva & Kuslys, Marius & Correia, Ricardo Fontes, 2023. "The role of influencer–follower congruence in the relationship between influencer marketing and purchase behaviour," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    11. Colmekcioglu, Nazan & Marvi, Reza & Foroudi, Pantea & Okumus, Fevzi, 2022. "Generation, susceptibility, and response regarding negativity: An in-depth analysis on negative online reviews," Journal of Business Research, Elsevier, vol. 153(C), pages 235-250.
    12. 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.
    13. Carlson, Brad D. & Donavan, D. Todd & Deitz, George D. & Bauer, Brittney C. & Lala, Vishal, 2020. "A customer-focused approach to improve celebrity endorser effectiveness," Journal of Business Research, Elsevier, vol. 109(C), pages 221-235.
    14. Ishita Chakraborty & Minkyung Kim & K. Sudhir, 2019. "Attribute Sentiment Scoring With Online Text Reviews : Accounting for Language Structure and Attribute Self-Selection," Cowles Foundation Discussion Papers 2176R, Cowles Foundation for Research in Economics, Yale University, revised Sep 2020.
    15. Bikram Jit Singh Mann & Yadvinder Parmar & Mandeep Kaur Ghuman, 2023. "A New Scale to Capture the Multidimensionality of Celebrity Image," Global Business Review, International Management Institute, vol. 24(6), pages 1251-1275, December.
    16. Xuan Gong & Yunchan Zhu & Rizwan Ali & Ruijin Guo, 2019. "Capturing Associations and Sustainable Competitiveness of Brands from Social Tags," Sustainability, MDPI, vol. 11(6), pages 1-20, March.
    17. Carlson, Keith & Kopalle, Praveen K. & Riddell, Allen & Rockmore, Daniel & Vana, Prasad, 2023. "Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 54-74.
    18. Daniel Gartner & Yiye Zhang & Rema Padman, 2018. "Cognitive workload reduction in hospital information systems," Health Care Management Science, Springer, vol. 21(2), pages 224-243, June.
    19. Christof Naumzik & Stefan Feuerriegel & Markus Weinmann, 2022. "I Will Survive: Predicting Business Failures from Customer Ratings," Marketing Science, INFORMS, vol. 41(1), pages 188-207, January.
    20. Boegershausen, Johannes & Datta, Hannes & Borah, Abhishek & Stephen, Andrew, 2022. "Fields of Gold: Web Scraping and APIs for Impactful Marketing Insights," Other publications TiSEM 5f1ed70a-48c3-422c-bc10-0, Tilburg University, School of Economics and Management.

    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:ejores:v:311:y:2023:i:1:p:316-332. 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: http://www.elsevier.com/locate/eor .

    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.