Assessing How the “Humanness” of Smart Voice Assistants (SVAs) Drives Consumer Satisfaction and Purchase Intent
Bibliographic record
Abstract
This study examines how interactions with smart voice assistants (SVAs) extend beyond functional use, as individuals develop human-like connections with these technologies. Drawing on the media equation theory and parasocial theory, the research investigates how perceived humanness, specifically anthropomorphism, autonomy, and suspension of disbelief, influences users' satisfaction and purchase intentions. Data were collected from 449 participants and analyzed using partial least squares structural equation modeling (PLS SEM). The findings reveal that anthropomorphism, autonomy, and suspension of disbelief significantly enhance users' sense of control (SOC) and emotional investment (EI). In turn, SOC and EI positively impact customer satisfaction and purchase intentions. Additionally, SOC and EI mediate the relationships between suspension of disbelief, autonomy, customer satisfaction, and purchase intentions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".