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Record W4404199475 · doi:10.1002/joom.1338

Targeting online sales through last‐mile delivery platform integration

2024· article· en· W4404199475 on OpenAlexaff
Kevin H. Park, Xiaodan Pan, Martin Dresner

Bibliographic record

VenueJournal of Operations Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsBusinessOrder (exchange)Last mile (transportation)Delivery systemMarketingChannel (broadcasting)Customer baseAdvertisingMileComputer scienceTelecommunicationsFinance

Abstract

fetched live from OpenAlex

Abstract We analyze channel integration between a last‐mile delivery platform and a general merchandise retailer in two distinct stages: (1) platform delivery access (PDA), where the retailer continues to offer standard delivery through its own website but directs customers to the platform's website for new same‐day delivery; and (2) integrated delivery access (IDA), where customers can continue to use same‐day delivery service at the delivery platform website but can purchase products in a single order with both same‐day and standard delivery options at the retailer's website. We perform a quasi‐experiment using consumer spending data from retailer, target, and delivery platform, Shipt. We find that PDA provides positive impacts to the delivery platform through increased sales. IDA, on the other hand, increases the retailer's online channel sales but does not impact the delivery platform's sales. Moreover, we find that the positive effects of PDA on the delivery platform's sales are stronger in markets where online grocery penetration is lower, indicating that the effects were likely driven by increased purchases for groceries. Finally, the positive effect of IDA on the retailer's online channel sales is stronger in markets where the retailer has a greater loyal customer base and online grocery penetration is lower.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.284
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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