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Online Customer Experience (OCE) In Textile Industry E-retailing: Novel Commercial Strategy To Expand Into The Current Market

2023· article· en· W4366549174 on OpenAlexaff
Rajat Sharma -, Praveen Awasthi, Ruchi Mishra -

Bibliographic record

VenueInternational Journal For Multidisciplinary Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsMinnow Environmental (Canada)
Fundersnot available
KeywordsBusinessMarketingTextile industryTextileValue (mathematics)Customer advocacyCustomer to customerCustomer retentionAdvertisingComputer scienceService (business)

Abstract

fetched live from OpenAlex

Online-Customer-Experience (OCE) can be characterized as a psychological connection between a consumer and a business. Engaging the customers brings them closer to the brand, company, and organization. OCE aids in the continuous demonstration of dedication to the customer and in the value’s creation. In today's uncertain business environment, it seems to be critical for businesses to retain their consumers close to them. In this research, the OCE in the textile-industry market was assessed. The research examined at and thus the variables affecting customer interaction, effects of potential disillusionment of customers. To achieve such, a qualitative approach involving 200 applicants was conducted in April 2023 using an online survey method. Allen Solley and Peter England would be used as instances to enquire online questionnaires about the online textile-industry.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.237
GPT teacher head0.551
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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