Production Chain Organization in the Digital Age: Information Technology Use and Vertical Integration in U.S. Manufacturing
Bibliographic record
Abstract
Advances in information technology (IT) may affect the organizational design of production. Exploiting the rapid diffusion of the internet in the United States, we assess the sensitivity of production chain organization to this innovation in IT access and use. Extending theories of the firm that recognize the importance of downstream transfers (selling as opposed to sourcing) and plural governance in organizational design, we predict IT-driven shifts in downstream vertical integration. In a detailed panel of Census Bureau data for over 5,600 manufacturing plants, we observe the extent of a production unit’s downstream transactions within the firm alongside concurrent sales to external customers—a mix we refer to as plural selling. Our main finding is that the use of the internet for external coordination precipitated a significant decline in downstream vertical integration across the manufacturing sector. Instrumental variables estimation points to a causal relationship but also heterogeneous treatment effects. Key drivers of plural organization, such as complementarities and constraints across differently governed transactions, help explain such heterogeneity, as does concurrent use of internal production management IT. Our study is the first study to leverage a plural governance framework and large-scale microdata to understand how U.S. production chain organization shifted in response to this rapid and far-reaching technological change. This paper was accepted by David Simchi-Levi, information systems. Funding: This research was performed at the Atlanta, Boston, and Cornell (supported by the Cornell Center for the Social Sciences) Federal Statistical Research Data Centers [Project 1069 (CBDRB-FY22-279)]. Support for the Research Data Centers network from the National Science Foundation [Grant ITR-0427889] is gratefully acknowledged, as is support from the Social Sciences and Humanities Research Council of Canada. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2019.01586 .
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".