Bibliographic record
Abstract
Abstract This paper examines the allocation of inventive effort in complex product systems. I argue that complex product systems, e.g., personal computers (PCs), are distinguished by functional interaction among several components, each guided by a relatively autonomous bundle of technical and economic characteristics. I try to explore whether the dynamics of such interactions between components of complex product systems can help us understand changes in the relative allocation of inventive effort. I advance and empirically test three hypotheses: (1) emergence of component constraints (bottlenecks) in product systems will trigger research and development (R&D) investment to resolve the constraints; (2) slack component firms have a strong incentive to invest in resolving component constraints; and (3) the incentive of slack component firms to invest in resolving component constraints is increasing in their prior sunk R&D investments in slack components. In sum, I argue that interactions between components in a product system conditions the R&D incentives of firms and also that the incentives are increasing in their prior investments or capabilities. Using product reviews from technical journals, I trace the constraint components in the PC from 1981 to 1998 and attempt to predict shifts in the allocation of inventive effort in the subsequent period. The empirical results strongly support all three hypotheses. This study highlights the paradoxical effect of modularity in complex product systems. Modular design architectures, while contributing to accelerating the pace of technical change, also tend to limit the economic benefits of firms' component R&D efforts, especially when different components technologies are progressing at different rates. This often creates an impetus to enlarge the scope of firm R&D activities beyond the component product markets that firms operate in. Other implications for R&D decision making are discussed. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".