Who Shares? Profiling Consumers in the Sharing Economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sharing platforms are becoming increasingly common, transforming how organisations and customers interact across diverse categories. While there is clear demand for the sharing economy, less is known about heterogeneity of consumer preferences and the varying demand that exists for sharing experiences across different categories of consumption. In order to help brands better understand who shares, this research takes a step forward in the profiling of users of the sharing economy. Drawing on social psychology, this research investigates how social norms can be employed as a form of social influence and nudge consumers to engage in higher levels of shared consumption. We find three clear segments of sharing consumers, representing 86% of all consumers: the mobility-focused sharer, the diverse-platform sharer, and the power-platform sharer. The last segment (accounting for 14%) comprises consumers who do not engage with sharing platforms. Moreover, social norms influenced the future behaviours of only one segment of consumers: the diverse-platform sharer. We discuss how sharing platform providers can better understand, target, and convert consumers to engage in sharing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it