Challenges for Corporate Reputation—Online Reputation Management in Times of Global Pandemic
Bibliographic record
Abstract
The issue of corporate reputation management in the time of accelerated digitization has been a subject of research by academics and practitioners for more than a decade. The aim of this study was to provide an insight into the issue of reputation management in the Internet environment in the time of global pandemic. As for the structure of the research, the study mapped two horizons of events, the first one being the onset of the pandemic in the first half of 2020, and the second one the period of cancellation of antipandemic measures after 24 months. The research was localized in the market of Central Europe, specifically in the online market of the Slovak Republic. This market synthesized two important factors, namely the highly developmental nature and at the same time the increased degree of restraint it experienced during the two years of the pandemic. A sophisticated online reputation analysis (sentiment analysis, analysis of reputation determinants, and data synthesis through the TOR indicator) was performed on a significant sample of e-commerce representatives, the results of which provided relevant findings on reputational challenges and reputational threats. Based on the findings, it can be stated that the market has adapted relatively quickly to the changed conditions. The pandemic represented a market opportunity rather than an existential threat for the subjects examined. It also played the role of an imaginary accelerator in the evolutionary transition from offline to online.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".