Impact of Sustainability Reporting on Financial Performance: A Multigroup Analysis of Jordanian Firms in High-Pollution and Low-Pollution Industries
Bibliographic record
Abstract
As global emphasis on environmental, social, and governance practices intensifies, sustainability reporting emerges as a critical tool for corporate transparency and accountability. The study aims to assess the impact of sustainability reporting on the financial performance of listed companies in Jordan. Using a quantitative approach, a total of 588 individuals were surveyed from low-pollution and high-pollution industries using purposive sampling techniques. Partial Least Square Structural Equation Modeling (PLS-SEM) was used to conduct analysis of the data with the aid of SMART PLS4 software. The study finds that the impact of sustainability disclosures on firms’ financial performance in Jordan differs significantly by both the type of disclosure and the pollution intensity of the industry the firms belong to. Environmental impact reporting (EIR) and social impact reporting (SIR) both have positive and significant effects on financial performance, especially in low-pollution industries, probably because of a perceived proactive and authentic integration of sustainability practices. However, governance impact reporting (GIR) shows a negative relationship with financial performance, which implies that such disclosures may be perceived as compliance-driven or not authentic. These findings indicate that the context of the sustainability reporting strategy is an important element in determining its effect on financial performance. The multigroup analysis (MGA) results help us to gain a better understanding of how different sectors leverage financial value from disclosing their sustainability activities. The study confirms that sustainability disclosure is not just a compliance requirement, but an instrument that can help firms improve their financial performance. Finally, we recommend that future research should investigate deeper psychological and social mechanisms likely to influence stakeholder responses across different sectors and countries within the region.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".