WhatAS the Big Idea? Multi-Function Products, Firm Scope and Firm Boundaries
Bibliographic record
Abstract
Products often bundle together many functions e.g., smartphones. The firm develops the big idea (which functions to bundle) and then chooses one supplier per function. We develop a model featuring holdup in which the firm's bargaining power declines in the number of suppliers. Greater scope as measured by the number of suppliers exacerbates holdup, but this is partially offset by the appropriate choice of vertical integration or outsourcing. Our main result flows from the empirical observation that the number of functions varies across products within an industry (firm heterogeneity). We introduce the notion of an 'ideas-oriented' industry in which more productive firms have higher marginal returns to introducing a new function. We show that more productive firms will (1) have more suppliers and (2) be more likely to integrate those suppliers. We take this to the data using a neural network to predict whether or not each of 29 million PATSTAT patent applications involves new/improved functions. We merge these patents with Capital IQ data on 55,000 companies and their supplier networks. We show that in industries where patents are skewed towards new or improved functions, more productive firms have more suppliers and are more likely to integrate these suppliers.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".