Bibliographic record
Abstract
When companies first plunged into e-commerce, they thought success meant cutting out middlemen. That approach didn't work, in part because e-businesses misunderstood the role of intermediaries. Middlemen are not costly, necessary evils. They solve problems for customers and, in so doing, they enable sales and create value for producers. INSEAD's Philip Anderson and Erin Anderson show how intermediaries are helping smart companies realize the promise of the Web. They explain intermediaries' nine ways of adding value, suggesting that three will change, three will survive in a new form, and three (reducing uncertainty about quality, preserving customer anonymity and tailoring offerings to customer needs) present growth opportunities. Middlemen can co-opt the Internet by offering services that would be too difficult for individual producers to provide. However, the authors caution, intermediaries must be open to new ways of doing business with suppliers and vice versa. The Web transforms but does not eliminate the advantages of the middleman's central lookout position. But what was once thought of as a straight distribution channel from supplier through middleman to customer is now more accurately described as a service hub. The player that takes the customer order ? possibly a Web site ? occupies the center and interacts with many partners. The authors specify appropriate, fair incentives (for example, because Ethan Allen has quasi-independent furniture stores that customers browse before buying directly from the manufacturer's Web site, the company automatically gives the nearest retailer a 10% tip). And they describe service-hub management that will generate enough trust to permit producers to get closer to customers ? indirectly. Philip Anderson is the
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.022 |
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".