Mining Brand Perceptions from Twitter Social Networks

Published Online:https://doi.org/10.1287/mksc.2015.0968

References

  • Aaker DA (1996) Measuring brand equity across products and markets. California Management Rev. 38(3):102–120.CrossrefGoogle Scholar
  • Archak N, Ghose A, Ipeirotis PG (2011) Deriving the pricing power of product features by mining consumer reviews. Management Sci. 57(8):1485–1509.LinkGoogle Scholar
  • Baird CH, Parasnis G (2011) From social media to social CRM: Reinventing the customer relationship. Strategy Leadership 39(6):27–34.CrossrefGoogle Scholar
  • Barnes NG, Lescault AM, Wright S (2013) Fortune 500 are bullish on social media. Charlton College of Business Center for Marketing Research, University of Massachusetts Dartmouth, http://www.umassd.edu/cmr/socialmediaresearch/2013fortune500/.Google Scholar
  • Bearden WO, Etzel MJ (1982) Reference group influence on product and brand purchase decision. J. Consumer Res. 9(2):183–194.CrossrefGoogle Scholar
  • Berens G, Van Riel CBM, Van Bruggen GH (2005) Corporate associations and consumer product responses: The moderating role of corporate brand dominance. J. Marketing 69(3):35–48.CrossrefGoogle Scholar
  • Berger J, Heath C (2007) Where consumers diverge from others: Identity signaling and product domain. J. Consumer Res. 34(2):121–134.CrossrefGoogle Scholar
  • Bijmolt THA, van de Velden M (2012) Multiattribute perceptual mapping with idiosyncratic brand and attribute sets. Marketing Lett. 23(3):585–601.CrossrefGoogle Scholar
  • Bottomley PA, Doyle JR, Green RH (2000) Testing the reliability of weight elicitation methods: Direct rating versus point allocation. J. Marketing Res. 37(4):508–513.CrossrefGoogle Scholar
  • Buhrmester M, Kwang T, Gosling SD (2011) Amazon’s Mechanical Turk a new source of inexpensive, yet high-quality, data? Perspect. Psych. Sci. 6(1):3–5.CrossrefGoogle Scholar
  • Cha M, Haddadi H, Benevenuto F, Gummadi KP (2010) Measuring user influence in Twitter: The million follower fallacy. Proc. Fourth Internat. AAAI Conf. Weblogs Soc. Media (AAAI Press, Menlo Park, CA), 10–17.Google Scholar
  • Childers TL, Rao AR (1992) The influence of familial and peer-based reference groups on consumer decisions. J. Consumer Res. 19(2):198–211.CrossrefGoogle Scholar
  • Crystal D (2001) Language and the Internet (Cambridge University Press, New York).CrossrefGoogle Scholar
  • Danaher PJ, Wilson IW, Davis RA (2003) A comparison of online and offline consumer brand loyalty. Marketing Sci. 22(4):461–476.LinkGoogle Scholar
  • Das SR, Chen MY (2007) Yahoo! for Amazon: Sentiment extraction from small talk on the Web. Management Sci. 53(9):1375–1388.LinkGoogle Scholar
  • Day GS (1975) The threats to marketing research. J. Marketing Res. 12(4):462–467.CrossrefGoogle Scholar
  • Day GS, Shocker AD, Srivastava RK (1979) Customer-oriented approaches to identifying product-markets. J. Marketing 43(4):8–19.CrossrefGoogle Scholar
  • Dillon WR, Frederick DG, Tangpanichdee V (1985) Decision issues in building perceptual product spaces with multi-attribute rating data. J. Consumer Res. 12(1):47–63.CrossrefGoogle Scholar
  • Duggan M, Ellison NB, Lampe C, Lenhart A, Madden M (2015) Social media update 2014. Pew Research Center, Washington DC, http://www.pewinternet.org/2015/01/09/social-media-update-2014.Google Scholar
  • El Gazzar N, Mourad M (2012) The effect of online communication on corporate brand image. Internat. J. Online Marketing 2(1):1–15.CrossrefGoogle Scholar
  • Escalas JE, Bettman JR (2003) You are what they eat: The influence of reference groups on consumers connections to brands. J. Consumer Psych. 13(3):339–348.CrossrefGoogle Scholar
  • Etter M, Plotkowiak T (2011) CSR communication strategies for Twitter. 61th Annual Conf. Internat. Comm. Assoc. (International Communication Association, Washington, DC), 731.Google Scholar
  • Fader PS, Winer RS (2012) Introduction to the special issue on the emergence and impact of user-generated content. Marketing Sci. 31(3):369–371.LinkGoogle Scholar
  • Goel S, Goldstein DG (2013) Predicting individual behavior with social networks. Marketing Sci. 33(1):82–93.LinkGoogle Scholar
  • Goel S, Watts DJ, Goldstein DG (2012) The structure of online diffusion networks. Proc. 13th ACM Conf. Electronic Commerce (ACM, New York), 623–638.CrossrefGoogle Scholar
  • Grabowicz PA, Ramasco JJ, Moro E, Pujol JM, Eguiluz VM (2012) Social features of online networks: The strength of intermediary ties in online social media. PLoS ONE 7(1):e29358.CrossrefGoogle Scholar
  • Green PE (1975) Marketing applications of MDS: Assessment and outlook. J. Marketing 39(1):24–31.CrossrefGoogle Scholar
  • Green PE, Carmone FJ, Smith SM (1989) Multidimensional Scaling: Concepts and Applications, Vol. 18 (Allyn and Bacon, Boston).Google Scholar
  • Hauser JR, Koppelman FS (1979) Alternative perceptual mapping techniques: Relative accuracy and usefulness. J. Marketing Res. 14(4):495–506.CrossrefGoogle Scholar
  • Hauser JR, Simmie P (1981) Profit maximizing perceptual positions: An integrated theory for the selection of product features and price. Management Sci. 27(1):33–56.LinkGoogle Scholar
  • Henderson GR, Iacobucci D, Calder BJ (1998) Brand diagnostics: Mapping branding effects using consumer associative networks. Eur. J. Oper. Res. 111(2):306–327.CrossrefGoogle Scholar
  • Huber J, Holbrook MB (1979) Using attribute ratings for product positioning: Some distinctions among compositional approaches. J. Marketing Res. 16(4):507–516.CrossrefGoogle Scholar
  • John DR, Loken B, Kim K, Monga AB (2006) Brand concept maps: A methodology for identifying brand association networks. J. Marketing Res. 43(4):549–563.CrossrefGoogle Scholar
  • Johnson MD, Hudson EJ (1996) On the perceived usefulness of scaling techniques in market analysis. Psych. Marketing 13(7):653–675.CrossrefGoogle Scholar
  • Kaul A, Rao VR (1995) Research for product positioning and design decisions: An integrative review. Internat. J. Res. Marketing 12(4):293–320.CrossrefGoogle Scholar
  • Kim AJ, Ko E (2012) Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. J. Bus. Res. 65(10):1480–1486.CrossrefGoogle Scholar
  • Kuksov D, Shachar R, Wang K (2013) Advertising and consumers’ communications. Marketing Sci. 32(2):294–309.LinkGoogle Scholar
  • Kwon ES, Sung Y (2011) Follow me! Global marketers’ Twitter use. J. Interactive Advertising 12(1):4–16.CrossrefGoogle Scholar
  • Lancaster K (1971) Consumer Demand: A New Approach (Columbia University Press, New York).Google Scholar
  • Lee TY, Bradlow ET (2011) Automated marketing research using online customer reviews. J. Marketing Res. 48(5):881–894.CrossrefGoogle Scholar
  • Lehmann DR, Keller KL, Farley JU (2008) The structure of survey-based brand metrics. J. Internat. Marketing 16(4):29–56.CrossrefGoogle Scholar
  • Ludwig S, de Ruyter K, Friedman M, Brüggen EC, Wetzels M, Pfann G (2013) More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates. J. Marketing 77(1):87–103.CrossrefGoogle Scholar
  • Lydon JE, Jamieson DW, Zanna MP (1988) Interpersonal similarity and the social and intellectual dimensions of first impressions. Soc. Cognition 6(4):269–286.CrossrefGoogle Scholar
  • Manning CD, Raghavan P, Schütze H (2008) Introduction to Information Retrieval, Vol. 1 (Cambridge University Press, New York).CrossrefGoogle Scholar
  • Mason W, Suri S (2012) Conducting behavioral research on Amazons Mechanical Turk. Behav. Res. Methods 44(1):1–23.CrossrefGoogle Scholar
  • McDaniel SW, Verille P, Madden CS (1985) The threats to marketing research: An empirical reappraisal. J. Marketing Res. 22(1):74–80.CrossrefGoogle Scholar
  • McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: Homophily in social networks. Annual Rev. Sociol. 27(1):415–444.CrossrefGoogle Scholar
  • Morry MM (2007) Relationship satisfaction as a predictor of perceived similarity among cross-sex friends: A test of the attraction-similarity model. J. Soc. Personal Relationships 24(1):117–138.CrossrefGoogle Scholar
  • Naylor RW, Lamberton CP, West PM (2012) Beyond the “like” button: The impact of mere virtual presence on brand evaluations and purchase intentions in social media settings. J. Marketing 76(6):105–120.CrossrefGoogle Scholar
  • Netzer O, Feldman R, Goldenberg J, Fresko M (2012) Mine your own business: Market-structure surveillance through text mining. Marketing Sci. 31(3):521–543.LinkGoogle Scholar
  • Pan Y, Li D-H, Liu J-G, Liang J-Z (2010) Detecting community structure in complex networks via node similarity. Physica A: Statist. Mech. Appl. 389(14):2849–2857.CrossrefGoogle Scholar
  • Pennebaker JW, Chung CK, Ireland M, Gonzales A, Booth RJ (2007) The Development and Psychometric Properties of LIWC2007 (LIWC.Net, Austin, TX).Google Scholar
  • Pereira HG, de Fátima Salgueiro M, Mateus I (2014) Say yes to Facebook and get your customers involved! Relationships in a world of social networks. Bus. Horizons 57(6):695–702.CrossrefGoogle Scholar
  • Peters I (2009) Folksonomies: Indexing and Retrieval in Web 2.0, Vol. 1 (Walter de Gruyter, Berlin).CrossrefGoogle Scholar
  • Schmalensee R, Thisse J-F (1988) Perceptual maps and the optimal location of new products: An integrative essay. Internat. J. Res. Marketing 5(4):225–249.CrossrefGoogle Scholar
  • Shocker AD, Srinivasan V (1979) Multiattribute approaches for product concept evaluation and generation: A critical review. J. Marketing Res. 16(2):159–180.CrossrefGoogle Scholar
  • Sonnier GP, McAlister L, Rutz OJ (2011) A dynamic model of the effect of online communications on firm sales. Marketing Sci. 30(4):702–716.LinkGoogle Scholar
  • Sprouse J (2011) A validation of Amazon Mechanical Turk for the collection of acceptability judgments in linguistic theory. Behav. Res. Methods 43(1):155–167.CrossrefGoogle Scholar
  • Steenkamp J-B, Van Trijp H (1997) Attribute elicitation in marketing research: A comparison of three procedures. Marketing Lett. 8(2):153–165.CrossrefGoogle Scholar
  • Steenkamp J-BEM, Van Trijp HCM, Berge JMFT (1994) Perceptual mapping based on idiosyncratic sets of attributes. J. Marketing Res. 31(1):15–27.CrossrefGoogle Scholar
  • Stephen A, Dover Y, Goldenberg J (2010) A comparison of the effects of transmitter activity and connectivity on the diffusion of information over online social networks. INSEAD Working paper, http://ssrn.com/abstract=1609611.Google Scholar
  • Tang C, Guo L (2013) Digging for gold with a simple tool: Validating text mining in studying electronic word-of-mouth (eWOM) communication. Marketing Lett. 26(1):67–80.CrossrefGoogle Scholar
  • Tirunillai S, Tellis GJ (2012) Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Sci. 31(2):198–215.LinkGoogle Scholar
  • Toubia O, Stephen AT (2013) Intrinsic vs. image-related utility in social media: Why do people contribute content to Twitter? Marketing Sci. 32(3):368–392.LinkGoogle Scholar
  • Urban GL, Johnson PL, Hauser JR (1984) Testing competitive market structures. Marketing Sci. 3(2):83–112.LinkGoogle Scholar
  • Wu S, Hofman JM, Mason WA, Watts DJ (2011) Who says what to whom on Twitter. Proc. 20th Internat. Conf. World Wide Web (ACM, New York), 705–714.CrossrefGoogle Scholar
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