Hey chatbot, why do you treat me like other people? The role of uniqueness neglect in human-chatbot interactions
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.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".