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Record W4323345803 · doi:10.1109/tem.2023.3247340

Perils and Merits of Cross-Channel Returns

2023· article· en· W4323345803 on OpenAlex
Armağan Özbilge, Elkafi Hassini, Mahmut Parlar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Engineering Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCannibalizationChannel (broadcasting)Extant taxonEconomicsProfit (economics)MicroeconomicsMonetary economicsEconometricsIndustrial organizationTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

In this article, we study the impact of cross-channel returns on a bricks-and-clicks dual-channel retailer's overall profit, individual channel prices, and individual channel demand under two scenarios: 1) exogenous returns and 2) refund-dependent returns. Our study reveals a number of interesting results. For example, when channel substitutability is high, accepting online purchased returns in the bricks-and-mortar store is likely to drive the in-store price up, despite a drop in the offline demand due to the cannibalization effect. In general, firms should allow cross-channel returns when channel substitutability is high, return handling cost is low, and self-channel returns are not hefty. Unlike the extant literature, we also see that bricks-and-mortar returns impact a multiple-channel retailer's optimal return policy. We are also able to verify that our main findings are fairly consistent under both exogenous and refund-dependent returns scenarios.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.572
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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