HOW ARE EMOTIONAL ATTACHMENT STRATEGIES CURRENTLY EMPLOYED IN PRODUCT-SERVICE SYSTEM CASES? A SYSTEMATIC REVIEW UNDERSCORING DRIVERS AND HINDRANCES
Bibliographic record
Abstract
Abstract Aiming to decouple value creation from resource consumption, the Circular Economy is considered an alternative to the current linear model of production and consumption. Among the innovative circular business models, Product-Service Systems (PSS) have been recognized as a possible route to achieve enhanced sustainability performance through the extension of product lifespans and the reduction of product substitution. However, PSS may lead to rebound effects due to less careful behavior during the use phase, which compromises product durability. Currently, the effect of non-ownership models on product care is not yet fully understood, nor are the strategies that could enable better product care. This research aims to deeper comprehend the consumer-product relationships in PSS solutions, as well as to shed light on the potential role of emotional durability in PSS development for product attachment. In order to do so, this paper analyses twelve Product-Service System cases derived from a systematic literature review, categorizing the emotional attachment strategies in each case, and identifying how these strategies might hinder or potentialize PSS solutions.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".