Corporate social responsibility, water management, and financial performance in the food and beverage industry
Bibliographic record
Abstract
Abstract Water management is an important issue for the food and beverage sector. Global media reports about conflicts between industries and stakeholders about water resources that hurt the food and beverage industry. This study addresses the gap in the knowledge about the connection between water management performance and the financial performance of companies in the food and beverage industry that exists because of a lack of empirical studies in the field of corporate water management and financial performance. Using structural equation modeling to analyze secondary corporate social performance data from KLD‐MSCI, secondary financial data from Compustat, and primary water management data, the results suggest that corporate social performance has a positive impact on water management performance and that water management performance influences the financial performance of the firms in the sample positively. We conclude that an inside‐out approach of corporate social responsibility addressing material issues, such as water in the food and beverage industry, helps to increase the financial performance in this industry. Academically, we contribute to the knowledge about the connection between water management and financial performance in the food and beverage industry. Furthermore, our results can be used by policymakers to implement standardized water indicators for the food and beverage industry. Finally, businesses can use the results of the study to improve their water‐related environmental performance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".