Implementation of Sustainability Strategies in Operations and Abnormal Stock Returns Under Uncertainty: Evidence from Companies Listed on the Vietnamese Stock Market During the COVID-19 Outbreak
Bibliographic record
Abstract
This study examines the effects of implementing sustainable strategies in operations on the abnormal stock returns of companies listed on the Vietnamese stock market under uncertain conditions, using an event study and difference-in-differences analysis. Daily trading data were obtained from 107 companies listed on the Vietnamese stock market from 2 January 2020 to 31 March 2020 (~6313 observations included in the sampling). Of these, 41/107 (38.3%) and 66/107 (61.7%) did and did not implement sustainability strategies in their operations, respectively. The feasible generalized least-squares regression model indicated a positive impact of the implementation of sustainable strategies in operations on abnormal stock returns of the companies during the COVID-19 pandemic (p < 0.01 in the context of the COVID-19 pandemic). The results underline the implementation of sustainability strategies in the operations of companies as a critical tool to mitigate damage under uncertain conditions, enhance resilience, and achieve long-term competitive advantages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".