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Record W4317911825 · doi:10.1093/jcr/ucad006

How Verb Tense Shapes Persuasion

2023· article· en· W4317911825 on OpenAlexafffund
Grant Packard, Jonah Berger, Reihane Boghrati

Bibliographic record

VenueJournal of Consumer Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPersuasionPsychologyVerbModerationMediationPresent tensePast tenseLinguisticsSocial psychologyCognitive psychologySociology

Abstract

fetched live from OpenAlex

Abstract When sharing information and opinions about products, services, and experiences, communicators often use either past or present tense (e.g., “That restaurant was great” or “That restaurant is great”). Might such differences in verb tense shape communication’s impact, and if so, how? A multimethod investigation, including eight studies conducted in the field and lab, demonstrates that using present (vs. past) tense can increase persuasion. Natural language processing of over 500,000 online reviews in multiple product and service domains, for example, illustrates that reviews that use more present tense are seen as more helpful and useful. Follow-up experiments demonstrate that shifting from past to present tense increases persuasion and illustrate the underlying process through both mediation and moderation. When communicators use present (rather than past) tense to express their opinions and experiences, it suggests that they are more certain about what they are saying, which increases persuasion. These findings shed light on how language impacts consumer behavior, highlight how a subtle, yet central linguistic feature shapes communication, and have clear implications for persuasion across a range of situations.

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.

How this classification was reachedexpand

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)0.009
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.201
GPT teacher head0.461
Teacher spread0.260 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations33
Published2023
Admission routes2
Has abstractyes

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