Bibliographic record
Abstract
In North America, automobile prices are largely determined through negotiation. Recognizing that some consumers have a strong aversion to negotiation, some manufacturers and dealers are now offering consumers the option of buying cars at a “no-haggle”, or fixed price. This dissertation consists of three essays which address how a fixed price alternative impacts both consumer behavior and firm strategies. The first essay explores the conditions under which a dealer would simultaneously offer a “no-haggle” Internet price and a negotiable price on the lot (which we term a dual-channel), and studies the marketing strategies adopted under this structure. We use consumer haggling cost as a key to understanding a dealer’s choice of pricing strategy. We find that a dual-channel is optimal for the dealer when there is sufficient diversity in consumer haggling cost. We also find that it is optimal for a dealer to specify a higher-than-cost “minimum acceptable price” to the salesperson as a price floor for negotiations. Surprisingly, a dual-channel may serve fewer customers while still being more profitable than a single channel structure. The second essay examines the competitive implications of a no-haggle pricing policy. By using Toyota’s fixed pricing policy in Canada as a natural experiment, we explore the impact of such a strategy on the prices and sales of Toyota and that of its close competitor, Honda. We find that the program has had important competitive consequences. While prices of both Toyota and Honda were higher in provinces with the program, there was an increase in Honda’s sales but with no effect on the sales of Toyota. The third essay determines the impact of a consumer’s bargaining behavior and information she collects on the final price paid. Using an extensive dataset, we find that a consumer’s negotiation skill and attitude toward negotiating significantly influences the negotiation outcome. In particular, consumers that enjoy negotiating have a greater propensity to search for price information which then allows them to obtain a better deal. In addition, we find that, while Internet users pay a lower price, the savings depend on the type of information collected by the buyer.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".