Technological innovation in trade-in supply chain: Enterprise operations and consumer reactions
Bibliographic record
Abstract
Trade-in services, coupled with technological innovation for product update, are widely adopted by businesses. However, the practical implications of this strategy for enterprise operations and consumer purchasing behavior remain unclear, necessitating further exploration of how firms should respond. This study investigates a manufacturer offering trade-in services by comparing two scenarios: one where the manufacturer implements technological innovation and another where it refrains from doing so. Through the development of decision-making models and a comparative analysis of game-theoretic results, we examine the effects on enterprise operations and consumer responses to technological innovation. Additionally, we conduct a factor analysis to assess the determinants of technological innovation’s impact. Our findings reveal that, under trade-in services, technological innovation enhances the manufacturer’s profitability but may also lead to supplier hitchhiking. Both new and existing consumers exhibit homogeneous responses to innovation; however, under certain conditions, technological innovation may trigger consumer resistance. Furthermore, trade-in services can generate a synergistic effect with technological innovation, amplifying both its positive and negative consequences. Based on these insights, we propose operational adjustments to mitigate the identified adverse effects. This research provides managerial guidance for optimizing decision-making and addressing consumer reactions when implementing technological innovation in trade-in supply chains.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".