Leveraging Sustainability Reporting in Higher Education Institutions—A Multidimensional Research Agenda
Bibliographic record
Abstract
Sustainability has become increasingly important to research and practice. In order to determine impacts, identify improvement potential and to disclose efforts towards sustainability, an organization needs appropriate reporting. Thus, sustainability reporting has become a topic of broader interest, for example, to assess own situations, enable benchmarking, communicate own efforts and improve trust. Although sustainability reporting is a complex issue, only limited research and guidelines for higher education institutions (HEI) are available. Accordingly, negative impacts occur such as regarding the standardization and, thus, the comparability of reports. This article describes and demonstrates how different approaches from research related to reporting (information systems research in particular) and sustainability can be transferred to the field of sustainability reporting in HEIs to leverage the applicability of such reports. As a result, classifications of existing indicators and methodical approaches are provided, which are based on the analysis of a campus management system and different reporting standards as well as reporting knowledge in general. These classifications indicate that financial aspects are often focused and environmental issues are neglected. Moreover, the findings emphasize the importance of further multidimensional research on different topics such as (re-)development of specific indicators for HEIs, (re-)design of campus management systems and extension of current reporting standards. Therfore, a research agenda—with 18 agenda items—that synthesizes the presented directions is proposed. This agenda can be used to position further research or to derive new and innovative research questions.
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 | 0.189 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.031 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.048 | 0.076 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".