Product variety and firm survival in the microcomputer software industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article provides an analysis of product variety and scope economies in the microcomputer software industry by using detailed firm‐level and product‐level information on firms' bundling of functionalities over application categories and computing platforms. We find that the management of product variety through the way different application categories are integrated in products and the platforms on which these products are offered can be as important as the significance of scope economies at the more aggregated firm level. Specifically, we find that there is little evidence of firm benefits from economies of scope in production, but there is substantial evidence that products benefit from economies of scope in consumption. In addition, we find that firms with products that encapsulate more application categories perform better, and those with products that cover more computing platforms perform worse. Finally, changes in product variety through new product introductions improve firm performance, but extensions to existing products hinder the performance of the firm and the product. We conclude that research in scope economies can benefit from a more detailed model of the evolution of product variety that includes data and analysis at the firm level and at the product level. Copyright © 2004 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 it