When does the “assistant” heuristic work? Examining the effect of AI job titles in tasks with varying criticalities on the use of conversational AI-based services
Bibliographic record
Abstract
Recent marketing trends involve companies using low-status job titles, such as "assistant" (e.g., Google Home Assistant), to label conversational AI agents. This strategy aims to activate an altruistic "assistant" heuristic and enhance users' willingness to use these AI agents. However, this paper—comprising one pretest (N=313), three experiments (N=307, N=300, N=308), and one partial least squares structural equation modeling (PLS-SEM) analysis (N=309)—demonstrates that the effect of this strategy on willingness to use is positive only when the task criticality is high. When the task criticality is not high, higher-hierarchy AI titles (e.g., "manager," "teacher," "analyst") generate greater willingness to use. The research examines three alternative serial mediation pathways—perceived warmth, perceived control, and perceived risks—to test for competing explanations alongside the focal serial mediation through perceived humanlikeness and competence. Across the four studies, the serial mediation via perceived humanlikeness and competence remained robust, even when controlling for alternative pathways and scenario realism (Study 3). The final model indicates that when task criticality is not high, increased perceptions of hierarchical status in conversational AI settings enhance perceived humanlikeness. This, in turn, boosts perceived competence, ultimately increasing users' willingness to use the AI. However, when task criticality is high, the effect reverses—higher-status AI is perceived as less humanlike and less competent, reducing users' willingness to engage with it.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".