The Impact of E-Commerce on Competition in the Retail Brokerage Industry
Bibliographic record
Abstract
This paper analyzes the impact of e-commerce on markets where established firms face competition from Internet-based entrants with focused offerings. In particular, we study the retail brokerage sector where the growth of online brokerages and the availability of alternate sources of information and research services have challenged the dominance of traditional brokerages. We develop a stylized game-theoretic model to analyze the impact of competition between an incumbent full-service brokerage firm with a bundled offering of research services and trade execution and an online entrant offering just trade execution. We find that as consumers’ willingness to pay for research declines, the incumbent finds it optimal to unbundle its offering when competing with the online entrant. We also find that the online entrant chooses a lower quality of trade execution when faced with direct competition from the incumbent’s unbundled offering. The analytical model motivates a unique field experiment placing actual simultaneous trades with traditional full-service and online brokers, to compare order handling practices and the quality of trade execution. In keeping with our analytical results, our empirical findings show a significant difference in the quality of execution between online brokerages and their full-service counterparts. We discuss the relevance of our findings for quality differentiation, price convergence, and profit decline in a variety of markets where traditional incumbents are faced with changes in the competitive landscape as a result of e-commerce.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".