Falling out with AI-buddies: The hidden costs of treating AI as a partner versus servant during service failure
Bibliographic record
Abstract
The swift integration of artificial intelligence (AI)-driven tools in various industries, such as virtual assistants, chatbots, and service robots, raises inquiries about consumer reactions to these emerging technologies. To promote acceptance and enhance service interactions, companies frequently market these technologies by fostering parasocial and anthropomorphic relationships: the roles of partner and servant are among the most prevalent. Yet, the precise influence these relationship roles have on consumer responses remains uncertain. While extant literature primarily shows a positive effect of treating AI as a partner, in the current research, we find a multifaceted adverse effect of anthropomorphic partner (versus servant) relationships in the context of service failure. Across four studies, the results demonstrate that when consumers perceive an AI assistant as a relational partner, it heightens their inclination to attribute the failure to themselves because of elevated self-expansion perceptions with the AI. Furthermore, within this relationship dynamic, users exhibit reduced intentions of utilizing the AI agent again, as a result of a decreased sense of self-efficacy. Finally, the undesirable effects of a partner relationship following a service failure can be mitigated by drawing attention to the AI's learning capabilites. The findings of our research highlight a potential caveat of an AI-as-partner relationship, thus advancing our understanding of consumer interaction with AI from a relational perspective.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".