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Record W4206734233 · doi:10.1093/jcr/ucab076

Expression Modalities: How Speaking Versus Writing Shapes Word of Mouth

2021· article· en· W4206734233 on OpenAlex
Jonah Berger, Matthew D. Rocklage, Grant Packard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Consumer Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsYork University
Fundersnot available
KeywordsWord of mouthModality (human–computer interaction)PsychologyDeliberationModalitiesExpression (computer science)EmotionalitySocial psychologyWord (group theory)Cognitive psychologyLinguisticsAdvertisingComputer scienceSociologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Consumers often communicate their attitudes and opinions with others, and such word of mouth has an important impact on what others think, buy, and do. But might the way consumers communicate their attitudes (i.e., through speaking or writing) shape the attitudes they express? And, as a result, the impact of what they share? While a great deal of research has begun to examine drivers of word of mouth, there has been less attention to how communication modality might shape sharing. Six studies, conducted in the laboratory and field, demonstrate that compared to speaking, writing leads consumers to express less emotional attitudes. The effect is driven by deliberation. Writing offers more time to deliberate about what to say, which reduces emotionality. The studies also demonstrate a downstream consequence of this effect: by shaping the attitudes expressed, the modality consumers communicate through can influence the impact of their communication. This work sheds light on word of mouth, effects of communication modality, and the role of language in communication.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.177
GPT teacher head0.439
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it