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Record W4388923439 · doi:10.5539/ijms.v15n2p61

Apparel Mass Customization Digital Natives: New Insights into Development and Technology Implementation

2023· article· en· W4388923439 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Marketing Studies · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsnot available
Fundersnot available
KeywordsMass customizationClothingPersonalizationOrder (exchange)BusinessComputer scienceManufacturing engineeringEngineering managementProcess managementMarketingKnowledge managementEngineering

Abstract

fetched live from OpenAlex

Despite advancements in manufacturing and information technologies along with innovative operating models and engineering designs, apparel mass customization (MC) has mostly not lived up to its expectations. The main goal of this study is to explore digital native apparel MC companies and establish relevant insights concerning development and technology implementation. The study starts by facilitating a comprehensive literature review to explore apparel MC and related technologies, combined with exploring its implementation in different successful digital native cases. Following a descriptive-analytical approach, this paper offered insights into current technology implementation and utilization in the apparel MC industry, and then classifies their practices from low to high technology adoption. Moreover, by exploring real world cases, the paper developed insights on technology application of MC, which can guide strategic directions in order to accelerate a successful implementation of MC in the apparel 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.

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)0.002
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 score0.721
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.284
Teacher spread0.267 · 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