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Record W4384524542 · doi:10.54097/fbem.v10i1.10237

Research on the Impact of E-commerce on Offline Retail Industry

2023· article· en· W4384524542 on OpenAlex

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.

Bibliographic record

VenueFrontiers in Business Economics and Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOmnichannelOnline and offlineRetail industryAnalyticsBusinessKey (lock)MarketingE-commerceBig dataData scienceComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This research paper investigates the impact of e-commerce on the offline retail industry, examining both the challenges and opportunities it presents. The research draws on various sources, including industry reports, academic literature, and a case study of Costco, to provide an in-depth analysis of the topic. The paper begins by exploring the evolution of e-commerce and its effects on offline retailers, followed by a discussion of strategies offline retailers can employ to adapt to the changing retail landscape. These strategies include adopting an omnichannel approach, enhancing in-store experiences, utilizing data analytics and AI, and fostering strategic partnerships. The paper concludes with an outlook on the future of the offline retail industry, suggesting that continual innovation and customer-centric approaches are key for success. The findings of this research can provide valuable insights for offline retailers seeking to navigate the rapidly evolving retail environment in the age of e-commerce.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
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 head0.098
GPT teacher head0.318
Teacher spread0.219 · 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