Impact of COVID-19 on financial performance of logistics firms: evidence from G-20 countries
Bibliographic record
Abstract
Purpose This study aims to contribute to the extant literature on logistics by investigating the interrelationship between the financial performance of listed logistics firms and the COVID-19 and compare the logistics firms’ financial performance of G-20 countries during the pandemic period. Design/methodology/approach To conduct the confirmatory analysis by testing the hypotheses formulated for this study, data have been collected from Bloomberg of all logistics firms from G-20 countries. This paper gathered the first quarter from 2010 until the last quarter of 2020 as the research sample to examine the pandemic impact on financial performance. Findings The results show that the financial performance of logistic firms was significantly higher during 2020. Overall, the country-wise findings corroborated with the main results and the financial performance of 14 countries’ logistic firms out of 20 ones analysed has been significantly elevated, during the pandemic period. However, this paper has found out a negative financial performance of the logistics firms during the COVID-19 period in six countries (Germany, Korea, Russia, Mexico, Saudi Arabia and the UK), which support the second proposition. Research limitations/implications The study’s results were important as they highlighted the role of logistics firms in offering insights to academics, practitioners, policymakers and logistic firms’ stakeholders. For future research, this paper suggests including some other variables that might influence firm performance and that have not been considered in this study, which is a limitation, and going more deeply into the logistics sector by comparing the financial performance of the sub-sectors. Practical implications As the importance of logistics services during the pandemic period is relevant, this study may provide significant insights because the logistics firms play a crucial role by anticipating to ensure the supply of essential items such as food, medicine, then supporting for the continuity of supply chains. The view of finance impacts during the pandemic may provide insightful perspectives for logistics companies, allowing them to understand those impacts and better prepare for likely disruption events such COVID-19 pandemic. Originality/value This paper is novel considering that it is unique in evaluating logistics firms’ financial performance from a global perspective, considering the context of this historical pandemic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".