Impact of supply chain transparency on the consumption of remanufactured consumer goods
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate supply chain transparency in the context of remanufactured consumer goods. Supply chain transparency (SCT) is increasingly gaining importance for the relationship between firms and their final customers. Applied to a remanufacturing context, SCT may entail providing detailed information regarding the remanufacturing and return process, which can then be shared with consumers to alleviate potential quality concerns, decrease perceived disgust and ultimately increase consumers’ intention to purchase remanufactured goods. Design/methodology/approach Leveraging supply chain transparency embedded in signaling theory as well as the marketing framework of the hierarchy of effects model, this study employs a vignette-based experiment to investigate how process information, who performed the remanufacturing (remanufacturing firm: original manufacturer or a third party), and product information, the timeframe between the original purchase and return (length of return: short vs long), impact consumers’ intention to purchase remanufactured goods. Findings We find that information about the length of return has a significant impact on consumers’ intention to purchase the remanufactured good, while who performs the remanufacturing does not impact their decision-making. Originality/value Our results provide an overview of the externalities of providing supply chain transparency through the means of blockchain information on consumer purchasing behavior for remanufactured goods. Using SCT to engage with end customers in consumer goods is in its infancy and our work provides a basis for firms to invest in this in the B2C context.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".