Marketing through the Coronavirus Crisis - How Marketers in Albania Deal with the Ongoing Crisis of Covid-19
Bibliographic record
Abstract
None of the 21st century economic hardships have posed such a significant threat on businesses as the global pandemic COVID-19. In Albania beyond the evident threat it is causing to the human well-being, the coronavirus is threatening the health of businesses. According to the Albanian Institute of Statistics, in the second quarter of 2020, GDP in volume terms has decreased by 10.2% compared with the second quarter of 2019. This crisis gives a reflection to how the businesses in Albania do marketing and manage marketing risk. The purpose of this paper is therefore to investigate the Albanian marketers point of view and actions within the ongoing crisis caused by covid-19. The study aims to understand how marketers are dealing with the situation right now and whether they are taking a proactive or reactive approach. There is no current evidence whether marketers are managing crisis with a reactive method, purely tactical or proactively with a strategic viewpoint. In line with the purpose of this study a qualitative research approach was taken to gain insights on marketing through the coronavirus crisis in Albania. The depth interviews conducted with marketing managers of four different industries collected rich information on handling the coronavirus crisis and concluded that marketers were unprepared and adopted a reactive approach when dealing with the covid-19 crisis. Further the Covid-19 crisis gave rise to a more attentive and sensitive tone towards digital marketing in strengthening relationships with both partners and consumers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".