An internet-Enabled Move to the Market in Logistics
Bibliographic record
Abstract
Logistics outsourcing has increased with the commercialization of the Internet, implying a reduction in the corresponding transaction costs. The Internet – with its universal connectivity and open standards – radically enhanced information technology (IT) capabilities, and we hypothesize this has reduced external transaction costs relatively more than internal governance costs. Using transaction cost theory as a lens, we examine whether the commercialization of the Internet coincided with a move to the market in logistics – one of the most connected industries in the economy. We estimate the relationship between IT and outsourced logistics in a production function based on two datasets from 1987 to 2008. We find that the effects of IT on outsourced logistics have changed in the post-Internet era. After the commercialization of the Internet, an industry’s own IT investment and outsourced logistics became complements whereas they were not before. It suggests that because of the unique characteristics of the Internet as an enabler, IT reduced external transaction costs relatively more than internal governance costs. Consequently, industries favored the market form of the provision of logistics. We also find similar impacts of customers’ IT investments on a focal industry’s outsourced logistics. Previous studies argued that IT led to the shift from hierarchies to markets, or provided indirect evidence through measures of firm size or integration. Using a production theory model our study provides systematic empirical evidence to support that the Internet enabled a move to the market in the provision of logistics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".