Hey chatbot, why do you treat me like other people? The role of uniqueness neglect in human-chatbot interactions
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
Resistance to chatbots is a real challenge that companies must overcome, although they (i.e., chatbots) have several advantages. Based on the stereotype content model, this research seeks to understand customer reactions in the context of human-chatbot interactions by integrating the concept of uniqueness neglect as a moderator of customer reactions to the competence of bank chatbots. A sample of 378 respondents was collected in France using the snowball sampling technique, and hypotheses were tested using SmartPLS. We find that chatbot competence does influence customer satisfaction, the latter of which in turn affects both recommendation intention and continuance intention. Further, we find that uniqueness neglect moderates the effect of chatbot competence on satisfaction such that the effect is stronger (weaker) when uniqueness neglect is low (high). We find that warmth does not have a significant moderating effect. This study is among the first attempts to understand customer reactions to interactions with bank chatbots and offers insightful theoretical and managerial implications of use to both academics and practitioners alike.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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