The digital transition of collaborative consumption: toward sharing Economy 4.0
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
Purpose The literature has scrutinized the impact of various Industry 4.0 (I4.0) on the sharing economy (SE) and collaborative consumption. These results remain fragmented, sparse and specific to a single technology, context industry or subset of the SE. To fill this gap in the literature, this paper aims to examine the potential impacts of various I4.0 technologies – such as the blockchain, artificial intelligence, big data, the Internet of Things and additive manufacturing – on SE, thereby advancing knowledge of these impacts on SE-focused firms. Design/methodology/approach A multi-stage Cochrane systematic literature review involving two independent coders and the research team, which conducted the content analysis of 37 topical publications. Findings The findings reveal that I4.0 technologies have six significant impacts on the SE, including (1) safety (enabled by safeguarded information transmission and secure identity management but hindered by unresolved transaction privacy issues), (2) trust (enabled by traceability, transparency, confidence machines, but limited by the persistency of trust), (3) decentralization (through lateral authority, while reintermediation constitutes a point of tension), (4) efficiency (through disintermediation, superior match-making capacity and predictive maintenance), (5) cost reduction (lowering transactions and operating costs and lowering prices for users) and (6) smart contracting (enabled by automation, and immutability). Originality/value These findings extend the research on the connection between SE and I4.0 from a non-technical perspective, particularly in the tertiary sector, and are relevant to management theory and practice.
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 | 0.001 | 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 | 0.001 |
| 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 it