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She Said, She Said: Differential Interpersonal Similarities Predict Unique Linguistic Mimicry in Online Word of Mouth

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  • Sarah G. Moore
  • Brent McFerran

Abstract

This research examines the antecedents, causes, and consequences of linguistic mimicry, which assesses how closely individuals match others’ word use. We examine mimicry of linguistic style (how things are said) and content (what is said) in online word of mouth (WOM). To our knowledge, this research provides the first demonstration of unique linguistic mimicry, where consumers engaging in online WOM differentially mimic other posters’ word use. Two experiments and one study using field data show that when consumers are personally similar to an individual who has previously posted (e.g., same gender), they mimic this individual’s positive emotion and social word use. When consumers are similar in status to an individual who has previously posted (e.g., same forum ranking), they mimic this individual’s cognitive and descriptive word use. This differential mimicry is driven by affiliation versus achievement goals, respectively, and affects consumers’ engagement in online WOM in terms of posting incidence and volume.

Suggested Citation

  • Sarah G. Moore & Brent McFerran, 2017. "She Said, She Said: Differential Interpersonal Similarities Predict Unique Linguistic Mimicry in Online Word of Mouth," Journal of the Association for Consumer Research, University of Chicago Press, vol. 2(2), pages 229-245.
    • Handle: RePEc:ucp:jacres:doi:10.1086/690942
    DOI: 10.1086/690942
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    Cited by:

    1. Jonah Berger & Matthew D Rocklage & Grant Packard, 2022. "Expression Modalities: How Speaking Versus Writing Shapes Word of Mouth [Affective and Semantic Components in Political Person Perception]," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 49(3), pages 389-408.
    2. Hamby, Anne & Kim, Hongmin & Spezzano, Francesca, 2024. "Sensational stories: The role of narrative characteristics in distinguishing real and fake news and predicting their spread," Journal of Business Research, Elsevier, vol. 170(C).
    3. Jonah Berger & Grant Packard & Reihane Boghrati & Ming Hsu & Ashlee Humphreys & Andrea Luangrath & Sarah Moore & Gideon Nave & Christopher Olivola & Matthew Rocklage, 2022. "Marketing insights from text analysis," Marketing Letters, Springer, vol. 33(3), pages 365-377, September.
    4. repec:oup:jecgeo:v:50:y:2023:i:2:p:236-254. is not listed on IDEAS
    5. Motoki, Kosuke & Suzuki, Shinsuke & Kawashima, Ryuta & Sugiura, Motoaki, 2020. "A Combination of Self-Reported Data and Social-Related Neural Measures Forecasts Viral Marketing Success on Social Media," Journal of Interactive Marketing, Elsevier, vol. 52(C), pages 99-117.
    6. Sung Youl Jun & Tae Wook Ju & Hye Kyung Park & Jacob C. Lee & Tae Min Kim, 2023. "Information distortion in word-of-mouth retransmission: the effects of retransmitter intention and source expertise," Asian Business & Management, Palgrave Macmillan, vol. 22(5), pages 1848-1876, November.

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