Determinants of online apparel mass customization: a decade in review
Bibliographic record
Abstract
Purpose Mass customization is a production process that allows consumers to customize products from an array of options to suit their preferences and needs and benefit from large-scale production efficiencies. In recent years, several apparel retailers have integrated customization into their online presence. While the benefits of online apparel mass customization (OAMC) are apparent, factors that determine the usage of the process are many. Therefore, it is important to explore these factors and understand the relationships between them and the impact on the intention to use OAMC. Design/methodology/approach A review of studies published in the last decade was conducted through the Scopus, Web of Science and JSTOR databases in September 2023. Peer-reviewed research articles published in the English language were included. These studies were carried out in the United States of America, Canada, Korea and China and addressed motivations and antecedents of OAMC technology. Findings The data were extracted, and the findings were synthesized. The review process enabled us to examine several theories and determinants of OAMC. The latter were categorized into the following themes: “consumer personality and psychology”, “consumer perceptions”, “consumer behaviour determinants” and “process, experience and product”. The influence of consumer personality traits, psychogenic needs, characteristics and other facilitating conditions emerged through the review. Originality/value The purpose of this paper is to study the various determinants of OAMC and thereby provide valuable information to businesses in OAMC domains to improve customized processes, understand consumers' motivations and develop marketing strategies that improve overall satisfaction with OAMC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".