Managing the Versions of a Software Product Under Variable and Endogenous Demand
Bibliographic record
Abstract
Software product versioning (i.e., upgrading the product after its initial release) is a widely adopted practice followed by leading software providers such as Microsoft, Oracle, and IBM. Unlike conventional durable goods, software products are relatively easy to upgrade, making upgrades a strategic consideration in commercial software production. We consider a two-period model with a monopoly software provider who develops and releases a software product to the market. Unlike previous research, we consider demand variability and endogeneity to determine the functionality of the software in the first and second periods. Demand endogeneity is the impact of the word-of-mouth effect that positively relates the features in the initial release of the product to its demand in the second period. We also determine the design effort that should be spent in the first period to prepare for upgrading the product in the second period—upgrade design effort—to tap into the possible future demand. Results show that the upgrade design effort can be lower or higher when there is more market demand uncertainty. We also show that the features of the product in its initial release and upgrade design effort can be complements as well as substitutes, depending on the strength of the word-of-mouth effect. The results in this paper provide insights into how demand-side factors (market demand variability or demand endogeneity) can influence supply-side decisions (initial features and upgrade design effort). A key insight of the analysis is that a high word-of-mouth effect helps manage the product in the face of demand variability.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".