The Nonlinear Impact of Promised Delivery Time on Online Purchasing
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Bibliographic record
Abstract
Problem definition: For e-commerce companies to assess how best to invest in improving delivery times, it is important to understand how improving delivery times affects customer demand. In collaboration with a business-to-business (B2B) e-commerce company, we study how the promised delivery time in a quote affects the customer’s purchase probability. Methodology/results: We use observational and experimental data from our partner with quote-level variation in promised delivery times. This allows us to estimate demand as a function of promised delivery time after flexibly controlling for customer, product, and vendor differences. We find that there is a large, robust effect of promised delivery time on demand: a one-day improvement in promised delivery time increases demand by 1.82%, equivalent to a 2.21% discount, comparable to prior findings in business-to-consumer retail contexts. Interestingly, using semiparametric analysis, we find that this effect is nonlinear: demand is not sensitive to promised delivery times of under a week but drops quickly when delivery is expected to take more than a week. Managerial implications: We find that timely delivery is important in a B2B setting, not just in fast-moving retail settings. We show that the largest improvements in demand are to be gained from investing in measures that can reduce the long tail of slow deliveries (e.g., avoiding stockouts and processing delays, ensuring geographic coverage of fulfillment centers) rather than reducing the delivery time of products that are already relatively fast to deliver. The results from our analysis were used by our partner to decide on opening new fulfillment centers and repricing their services given the new fulfillment center network (because customers are willing to pay more for faster delivery). History: This paper has been accepted as part of the 2025 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1335 .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it