Places as authentic consumption contexts
Bibliographic record
Abstract
Abstract Consumers build social capital through purposeful consumer–place interactions. Airbnb claims that consumers want to “experience a place like [they] live there.” Previous research concentrates primarily on authenticity of objects, brands, and people, with limited development of place authenticity as a concept. But place authenticity represents an increasingly important marketing concept as consumers today, particularly millennials (Schulz, P. (2015, August 8). Not just millennials: Consumers want experiences, not things. Adage. Retrieved from https://adage.com/article/digitalnext/consumers-experiences-things/299994/ ), value experience over “stuff.” Authenticity provides an important place characteristic that if perceived, potentially unlocks a truly valuable consumer experience. Consequently, the research presented here develops an auxiliary theory of place authenticity (PA). The theory proposes a second‐order factor indicated by three coordinate subdimensions. Phase I of the research consists of five studies that develop PA, explore its dimensionality, and confirm the PA scale's construct validity. Phase II of the research involves a sixth study, which examines a set of hypotheses that begin to establish PA's nomological net. The results shed light on the psychology by which consumers extract value from experience and into ways marketing efforts can build effective place–value propositions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".