Navigating the warranty Landscape: Pricing and policy strategies by considering Consumers’ preferences
Bibliographic record
Abstract
• Pricing extended warranties in manufacturer-TPSP competition is addressed. • We consider consumers’ loyalty, tendencies, and risk attitudes. • Market competition and service breadth are taken into account in our analyses. • We investigate the primary parameters that affect TPSP’s pricing policies. • The sensitivities of pricing policies to cost escalation are analyzed. Given the importance of Extended Warranty Services (EWS), this study develops a mechanism to assist providers with pricing and policymaking in a competitive environment. We consider a manufacturer and a Third-Party Service Provider (TPSP), where the manufacturer needs to price its product and EWS, and the TPSP maximizes its profit by offering its EWS at the optimal price. Employing a game theory approach, we analyze how differences in service offerings and consumer preferences, including brand loyalty and risk aversion, influence the competitive dynamics. We use a consumer choice model to capture consumer preferences and their utility by analyzing the demand for each provided service. The results reveal that consumer preferences for EWS, brand loyalty, product failure rates, and risk aversion significantly affect service demand and profits. Likewise, the results uncover insights into how different market conditions and consumer preferences influence the manufacturers’ and the TPSPs’ optimal pricing decisions. For instance, enhancing brand loyalty can result in a 5.82% increase in the manufacturer’s EWS demand and a 24.6% increase in profit. Conversely, the TPSP can experience a 2.85% decrease in demand, leading to a 6.8% reduction in profit. These findings provide actionable insights for manufacturers and TPSPs aiming to design effective service policies and pricing strategies in a competitive market.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".