Omnichannel Services: The False Premise and Operational Remedies
Bibliographic record
Abstract
The notion of omnichannel, an integration of brick-and-mortar stores with online channels, has been thriving in recent years and is reforming the traditional service industry. Many service chains, such as Starbucks and McDonald’s, established omnichannel capability by allowing customers to order online in advance before visiting stores for pickup. The premise of omnichannel services is that when customers take advantage of the low-cost-of-waiting online channel, both their utility and the provider’s revenue will increase. Although simply adding an online-ordering option to the conventional walk-in model stimulates revenue, it also inflicts interference on the walk-in channel. We show that online ordering inadvertently reduces customers’ individual utility and social welfare when both channels are used in equilibrium. Moreover, the less it costs to order and wait online, the more the social welfare is reduced. We then evaluate two industry state-of-the-art operational remedies: regulating the use of the online channel and establishing channel-dedicated capacities. Although both remedies may improve the throughput over the walk-in-only service or even the first-come-first-served omnichannel service, they are unlikely to achieve this without jeopardizing the social welfare. We thus propose prioritizing walk-in customers and show that such prioritization can deliver this premise—that is, simultaneously benefiting the service provider and customers in comparison with the conventional walk-in-only service when both channels are used in equilibrium. Our results highlight that creating an efficient marketplace requires synergy between innovative technology and effective operational strategies. This paper was accepted by Victor Martinez de Albéniz, operations management. Funding: Y. Li was supported in part by the Hong Kong Research Grants Council General Research Fund [Project 14505820]. Supplemental Material: The technical supplement and online appendix are available at https://doi.org/10.1287/mnsc.2022.4416 .
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Operations management model of omnichannel service queues.
It develops operational-management theory for omnichannel services.
Operations-management analysis of omnichannel retail services, not research systems.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".