Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot
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
Abstract Consumers sometimes describe their experience of interacting with artificial intelligence‐based human‐like chatbots as creepy. This study investigates the antecedents of creepiness (i.e., the chatbot's usability, privacy concerns, and user variables such as technology anxiety and the need for human interaction) and its impact on consumer loyalty. Grounded in the technology paradox, it deepens the understanding of creepiness in light of the theoretical underpinnings of the privacy paradox and privacy cynicism. Presented with the task of obtaining a car insurance quote, 430 consumers participated in a simulation involving interaction with a chatbot, followed by a questionnaire. The findings show that creepiness decreases loyalty and indirectly impacts it through trust and negative emotions. While usability reduces perceptions of creepiness, privacy concerns raised by the interaction with the chatbot increase creepiness, which is positively associated with consumer traits (i.e., technology anxiety and need for human interaction). The main contribution of the research lies in its focus on creepiness, a concept under‐researched in the marketing literature, and which can be seen from the perspective of a coping mechanism for consumers’ privacy concerns. This paper provides practical implications to orient managers in the design and implementation of chatbots, as a promising touch point to build customer loyalty.
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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 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