Digital Technologies to Minimize the Impact of the Covid-19 Pandemic in Restaurant Sector
Bibliographic record
Abstract
At the end of the first quarter of 2020, there were signs that the year would be with many difficult challenges. The emergence of a new pandemic, a coronavirus (SARS-CoV-2), known as COVID-19, shivered the planet, in many ways, from personal to professional, economic and financial, affecting everything and everyone, causing a general quarantine around the world. The restaurant sector was no exception and following this unexpected situation, this study aims to understand how information and communication technology (ICT) is being used in the sector and how it can help to response to its current needs, without neglecting public health and avoiding insolvencies or dismissals, looking for digital solutions that can guarantee the efficiency of the sector, always respecting the costumer's experience. Therefore, a study is proposed to analyze how ICT can contribute to the recovery of the restaurant sector and how it can be incorporated, having as the main question ”How can technology be used to minimize the impact of the Covid-19 pandemic on the restaurant sector?”. A qualitative methodology was applied to the present study, having as sample several restaurants, in the north of Portugal of different types, to better understand how the introduction of technologies has been done, the impact of COVID-19 in ICT adoption and the actual needs on digitalization and technology, both in service and delivery and in internal and external communication. As preliminary results, we can identify some reluctance to introduce technologies and digitalization in the sector, with much to be explored in the sense of digitalization. We also verified that the biggest investments in this direction are on billing and on Enterprise Resource Planning systems. Furthermore, this study also presents some proposals for digital solutions that can assist this sector.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".