Product Upgrades with Stochastic Technology Advancement, Product Failure, and Brand Commitment
Bibliographic record
Abstract
Brand commitment and the risk of product failure play an important role in the timing of upgrades for a durable product in the presence of stochastic technology advancements. Higher brand commitment makes a firm less vulnerable to sales erosion in an incumbent product due to a technology lag. However, the firm is more susceptible to profit loss from the risk of a failed product. We show that under these circumstances, a firm's optimal upgrade strategy is characterized by a threshold policy based on the level of pent‐up demand for their next generation product. Contrary to previous research, we find that a threshold policy based on technology may be suboptimal, since the risk of product failure is nonmonotonic in terms of the technology lag. We extend the model to determine whether a firm should offer a temporary price reduction and show that price promotions can be used to mitigate the risk of lost sales associated with a risky product upgrade. We perform numerical experiments to examine the impact of brand commitment under different market scenarios related to the stochastic dynamics of technology advancements. The implications of the findings are discussed in the context of the smartphone industry.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".