How to Grow the Sharing Economy? Create Prosumers!
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
The sharing economy has changed many rules of business. One of those rules is the role of the firm and – importantly – the role of consumers, who can perform two roles and become both providers and consumers, i.e. “prosumers”. Therefore, the key network effect to leveraging the power of the sharing economy is for one-sided users, those who are consumers (e.g., Airbnb guests) or providers (e.g., Airbnb hosts), to add the second role and perform as providers and consumers and become prosumers (e.g., those who are Airbnb guests and hosts). Surprisingly, no studies have investigated this important phenomenon and measured how one-sided users may become prosumers. An online survey of 305 Airbnb users showed that trust and gratitude had a significant positive influence on service providers’ and consumers’ intentions to adopt the respective other role and become prosumers, and that those with high gratitude and trust had the highest intentions to become prosumers. However, consumers and providers differed markedly in how trust and gratitude influenced their intention to become prosumers. This study expands our understanding of trust and gratitude and highlights the potential for sharing platforms to create prosumers from both pools of one-sided users. Furthermore, it also makes a valuable contribution to the prosumer and sharing economy literatures by being the first to empirically measure users’ intentions to become prosumers in the sharing economy. We discuss the implications of the findings for practitioners, and suggest how future research could help leverage the sharing economy.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 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