Spatial density and ambient scent: effects on consumer anxiety
Bibliographic record
Abstract
Purpose – This replication and extension of Hirsch and Gruss examines the impact of spatial density and ambient scent on consumers' spatial perception and anxiety. The paper aims to discuss these issues. Design/methodology/approach – A 2 (spatial density: high, low)×3 (ambient scent: no scent, scent associated with spaciousness, scent associated with enclosed spaces) between-participants experimental design was implemented in a laboratory setting. A pretest determined scent selection and manipulation checks were successful. Findings – Spatial perception was influenced by spatial density, but not ambient scent. Ambient scent and spatial density interacted, such that consumers' anxiety levels significantly increased under conditions of low spatial density combined with an ambient scent associated with spaciousness, and directionally increased under conditions of high spatial density combined with ambient scent associated with enclosed space. Research limitations/implications – This research was conducted in a laboratory setting in order to increase experimental control. An exploration of the strength of the observed effects in a field (retail) setting would be insightful. Practical implications – Results of this study suggest that retailers need to consider both spatial density and choice of ambient scent carefully in order to reduce consumers' anxiety levels. Originality/value – This research is one of the few to consider the impact of spatial density and ambient scent on consumers' anxiety levels. The use of a between-participants design and the experimental manipulation of both spatial density and ambient scent results in a more rigorous test of the scent – anxiety relation observed in previous research.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".