Life after death? Study of goods multiple lives practices
Bibliographic record
Abstract
Purpose Marketing scholars have devoted little attention to the study of practices which grant multiple lives to goods. However, these practices can considerably extend products lifecycles with far-reaching implications for traditional retailers and the economy. Accordingly, this paper aims to provide scales for perceived impact and motivations of goods multiple lives practices and to investigate the influence of impacts on motivations. Design/methodology/approach A qualitative phase (three discussion groups and 15 in-depth interviews) identified consumers’ motivations and perceived impacts of goods multiple lives practices. Two online surveys were then conducted on online panels, involving more than 2,200 consumers, to develop the measurement scales and test the structural model. Findings Results show that impacts measured only marginally influence economic motives but account significantly for a broad range of other motivations (ecological, protester and social contact motives). Research limitations/implications The study design is cross-sectional, therefore lacking causality. Replication studies could cross-validate the findings by means of experimental research. Practical implications The findings may prove of specific interest to marketers and organizations in the goods multiple lives sector seeking to harness consumer interest in these types of practices for reasons above and beyond lone economic incentives. Originality/value This study is innovative in two regards: it explores a relatively under-theorized field in marketing, namely, goods multiple lives practices; and it proposes a challenging theoretical perspective which supposes that consumers’ perceived impact of their practices plays a significant role in motivating them to engage in practices of the like.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".