Mapping Corporate Sustainability and Firm Performance Research: A Scientometric and Bibliometric Examination
Bibliographic record
Abstract
Corporate sustainability has garnered increasing attention within the business community as corporations communicate to influence their stakeholders to build sustainable relationships. There has been a surge in research exploring its connection to firm performance, but existing studies lack a cohesive and concentrated approach. The aim of this study is to explore the trends of growth of publications; gauge the annual growth rate, annual ratio of growth, relative growth rate, doubling time, and scientific production index; predict future production levels; and look at the relationship between corporate sustainability and firm performance by analysing the literature as well as identifying clusters and links with the Sustainable Development Goals (SDGs). The top countries contributing to the research were China, India, and the United States, accounting for over 45% of the global publications. The study analysed a focused corpus of 65 documents from the Scopus database on specific subfields of corporate sustainability and firm performance, identifying five main thematic clusters related to environmental performance, financial performance, corporate sustainability reporting, corporate social performance, and green supply chain management, with significant citations related to 17 SDGs. The annual growth rate (AGR) of publications was found to be −2.88%, with an average of 4.06 publications per year. The relative growth rate (RGR) decreased from 0.69 in 2010 to 0.36 in 2023, and the doubling time (Dt.) increased from 1.00 in 2010 to 1.93 in 2023. Employing structured methods and the PRISMA protocol, this scientifically rigorous study points towards identification of research themes linking sustainability practices to firm performance. Exponential smoothing (Holt’s linear trend model) is employed to project future research output within the field. The significant trends include an increase in publication frequency since 2017, indicating a growth phase in the research field. The findings highlight the need for greater investigation from developing countries and the importance of integrating sustainability considerations into business strategies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.033 | 0.040 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".