Should chatbots use dialects? Exploring the influence mechanism of chatbot language form on value co-creation intention
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.
Bibliographic record
Abstract
Incorporating dialects into chatbot interactions is crucial for building stronger connections with users and promoting value co-creation, especially in contexts where personalisation is prioritised. This study draws on social cognitive theory, social presence theory, and social identity theory to investigate how the language form used by chatbots affects individuals’ value co-creation intention. Across four distinct experiments, we find that dialects, as opposed to standard language, considerably enhance users’ value co-creation intention. This impact is driven by heightened perceived warmth, perceived competence, and social presence. Furthermore, the study emphasises the differing effects based on group membership, showing that dialect usage positively influences perceived warmth, competence, social presence, and value co-creation intention, but only within in-group contexts. These results point out the power of dialects as cultural markers in human-AI interactions, offering valuable insights for designing more engaging and culturally resonant chatbots.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it