Brand Communication in Times of Crisis: A Comparative Study of Saudi Brands’ Digital Messages on X During and After the COVID-19 Pandemic
Bibliographic record
Abstract
Brand communication during public crises plays an essential role in building sustainable and strong relationships with customers. During the COVID-19 pandemic, Saudi brands, like all brands around the world, needed to devise novel survival strategies. This study explores and contrasts brand communication strategies both during and after the pandemic. The study focuses on the peak of the pandemic, which coincided with the holy month of Ramadan. It analyses two datasets comprising brands’ Twitter content during Ramadan 2020 and Ramadan 2024 using Taylor’s six-segment advertising strategy wheel. The sample included a total of 3021 tweets from 37 companies across five industrial sectors. The results reveal the ritual view with social segment dominated the tweets in both periods and variances in segment details at the sector level. Brand voice was found to have shifted from sympathetic in the first period to energetic in the second period, reflecting changing circumstances.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".