The digital transition of collaborative consumption: toward sharing Economy 4.0
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.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".