Agile Entrepreneurship Innovation in Fashion Design Thinking During COVID-19 and Beyond: Reimagine Education to Create Skills for Fashion Business
Bibliographic record
Abstract
Purpose: The COVID-19 crisis had a severe impact on University education (on-line learning, off-campus examinations). In addition, the COVID-19 pandemic raises questions about the quality of education and training in a number of disciplines, like fashion design, where social entrepreneurship opportunities and in-situ functionalities are essential for a quality curriculum. Hence, to remain relevant and innovative, fashion design thinking will need to reimagine education in order to create skills for e-entrepreneurship and prepare fashion entrepreneurs for e-business. Therefore, new concepts for fashion design thinking for innovation and e-entrepreneurship have to introduce in an e-classrooms curriculum. Methodology: This study employed the use of questionnaires to collect quantitative data and structured interviews to compile qualitative data (opinions) of two main groups of fashion design professionals: (a) clothing merchants and manufacturers, (b) fashion design green entrepreneurs. In particular, linear regression used to analyze the quantitative data (SPSS functionality) and data analytics software (QSR NVivo) adopted to encode the answers from the interviews. Findings: Findings obtained in this study show that –with the admission of agile entrepreneurship superiority and functional solution in crises like COVID-19- the green entrepreneurs are better positioning are better prepared to withstand the current COVID-19 or future crisis. Hence the requirement to integrate green entrepreneurship courses into the fashion d esign curriculum will be proposed to create innovation and value in fashion design thinking. Originality and value: This study inserts itself in a multidisciplinary field, mainly composed of four disciplinary areas: “fashion design thinking and education”, “digital transformation”, ”green entrepreneurship”, and “work-from-home entrepreneurship”. The introduction of the new term “Agile entrepreneurship” as a new disciplinary concept in fashion design curriculum is also crucial. Research limitations: The main limitation of this study is related to the definition of “Agile entrepreneurship” itself in COVID-19 and beyond work-from-home era, as bibliography still diverges on this subject.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".