Influence of sustainability reports on social and environmental issues: bibliometric analysis and the word cloud approach
Bibliographic record
Abstract
The aim of the present study is to analyze how corporate sustainability reports address socio-environmental issues and business development through bibliometric analysis. The search led to 53 articles indexed in the ScienceDirect database between 2012 and 2017. A bibliometric analysis was applied to sustainability reports and to several topics, namely: “sustainability report” (SR) and “corporate sustainability” (CS), triple bottom line (TBL), eco-innovation in business (ECO), and “global reporting initiative” (GRI). The word cloud approach was applied to each keyword in the quantitative analysis. Annual publication frequency was applied to identify the year accounting for the largest number of publications. The target of the descriptive analysis applied to the sample was determined; it consists in metrically determining the frequency of each variable. The inferential analysis compared the means recorded for the subsets of the sample; it is a technique commonly used to investigate data. Friedman’s test was used to compare the behavior of the research groups. The keywords sustainability, business, reporting, environment, social, and performance were found. These words appeared in most of the analyzed articles; they represented the conceptual core of each topic involved in the “sustainability report” (SR). Based on the selected articles, companies surveyed over the years have incorporated sustainability concepts into their strategic planning to ensure the satisfaction and needs of future generations. The disclosure of information available in sustainability reports has become a marketing instrument that may clearly provide evidence of business activities or inactivity.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".