Pandemic pains to Instagram gains! COVID-19 perceptions effects on behaviours towards fashion brands on Instagram in Sub-Saharan Africa: Tech-native vs non-native generations
Bibliographic record
Abstract
This study represents a novel attempt to investigate the cascading effects of COVID-19 perceptions onto behavioural patterns towards fashion brands on Instagram and across two generations technative vs tech non-native) in a Sub-Saharan African context.We drew our study on a sample of 338 Instagram users that experienced fashion brands on Instagram in two Sub-Saharan African countries: Uganda and Nigeria.We used partial least square structural equation modelling (PLS-SEM) to test the hypothetical model.We found that COVID-19 perception positively predicted enjoyment and usefulness, leading to more satisfaction with fashion brand accounts on Instagram and hence greater intention to follow and recommend those accounts.Finally, running a multigroup analysis (MGA), we found the effects of COVID-19 perceptions pronounced into both intentions to follow and intention to recommend via the sequence of mediators: enjoyment and satisfaction were only valid amongst the tech-native generational cohort.Our research suggested a new generational categorisation based on technology nativity -offering a new direction of generational studies in digital marketing communications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".