When Uber Eats its own business, and its competitors' too: Resource exclusivity and oscillation following platform diversification
Bibliographic record
Abstract
Abstract Research Summary How will a platform firm's diversification affect its existing business? Using datasets on the rideshare and food delivery businesses in New York City, we find that the launch of Uber Eats reduced Uber's and Lyft's rideshare trip volumes, but these effects were weaker during rush hours. Additional theoretical and empirical analyses suggest that, while platform diversification enables complementors to share some resources across businesses, it may also create opportunities for complementors to oscillate other complementary resources, thereby diverting complementor resources in the existing business from both the diversifying and competing platform firms. Such sharing‐enabled resource oscillation may be due to resource exclusivity at the transactional level and the lack of control by platform firms over resources at the organizational level. Managerial Summary We investigate how Uber's and Lyft's rideshare business was impacted by Uber's diversification into the food delivery business with the launch of Uber Eats in Manhattan, New York City. We find that, compared to geographic zones where no restaurant joined Uber Eats, zones where a significant proportion of restaurants joined Uber Eats experienced a relative reduction in rideshare trip volumes for both Uber and Lyft. Our results suggest that platform firms should be aware of the hidden costs of diversification due to their lack of control over gig economy participants (e.g., rideshare drivers). In addition, managers should be mindful of the diversification moves made not only by their own firm but also by competing firms.
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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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".