Watch Your Tone: How a Brand's Tone of Voice on Social Media Influences Consumer Responses
Bibliographic record
Abstract
Social media platforms enable firms to communicate directly and often publicly with individual consumers. In this research, comprising four online studies, the authors investigate how the tone of voice used by firms (human vs. corporate) influences purchase intentions on social media. Findings suggest that a human tone of voice is not always the firm's best option. Study 1a (N = 174) shows that using a human voice, instead of the more traditional corporate voice, can increase a consumer's hedonic value on social media and also purchase intentions. However, that influence of a human voice on purchase intentions is stronger when the consumer is looking at a brand page with a hedonic goal in mind (versus a utilitarian one). Study 1b (N = 342) shows that the presence of several negative comments about a brand on social media acts as a boundary condition, nullifying the influence of a human voice on purchase intentions. Studies 2a (N = 154) and 2b (N = 202) show in different settings that using a human voice can even reduce purchase intentions in contexts of high situational involvement, due to perceptions of risk associated with humanness. The results contribute to the literature surrounding the effects of conversational human voice, while also providing managers with a set of guidelines to help inform and identify which tone of voice is best adapted to each communications scenario.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".