Battle of the Retail Channels: How Product Selection and Geography Drive Cross-Channel Competition
Bibliographic record
Abstract
A key question for Internet commerce is the nature of competition with traditional brick-and-mortar retailers. Although traditional retailers vastly outsell Internet retailers in most product categories, research on Internet retailing has largely neglected this fundamental dimension of competition. Is cross-channel competition significant, and if so, how and where can Internet retailers win this battle? This paper attempts to answer these questions using a unique combination of data sets. We collect data on local market structures for traditional retailers, and then match these data to a data set on consumer demand via two direct channels: Internet and catalog. Our analyses show that Internet retailers face significant competition from brick-and-mortar retailers when selling mainstream products, but are virtually immune from competition when selling niche products. Furthermore, because the Internet channel sells proportionately more niche products than the catalog channel, the competition between the Internet channel and local stores is less intense than the competition between the catalog channel and local stores. The methods we introduce can be used to analyze cross-channel competition in other product categories, and suggest that managers need to take into account the types of products they sell when assessing competitive strategies.
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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 | 0.009 |
| 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 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".