Bibliographic record
Abstract
Purpose This paper aims to provide a stakeholder analysis of the environmental management strategies and a two‐dimensional (economic and environmental) performance of an Australian energy company that seeks environmental excellence. Unlike the dominant largely positivistic studies which seek an association between environmental and financial performance, the paper aims to use the richness of a case study methodology to gain a deeper understanding of how environmental concerns are handled and what outcomes in terms of environmental and economic performance are achieved. Design/methodology/approach An in‐depth case study approach involving interviews, archival material and site visits is used in this paper. It starts with a brief engagement with the largely positivistic literature, highlighting the major deficiencies of this scholarship and then presents a more interpretive empirical analysis using an Australian energy company. Findings The paper finds that there are socio‐political processes that are enlisted to control, monitor, and instil discipline in the organization's pursuit of its social initiatives, which help to improve both its financial and environmental performance. Practical implications The paper provides evidence that environmental and economic performance are not always mutually exclusive, and corporate entities can excel in both simultaneously. The paper also provides evidence that the environmental strategies may be overt attempts at pushing the socio‐political agenda of the dominant stakeholder group. What seems like a win‐win situation may only represent a political‐ethical attempt to promote environmentalism in the Australian energy sector. Originality/value This paper uses a two‐stage investigation process to extend one's understanding of the relationship between corporate environmental and financial performance. First, evidence of improving environmental and financial performance of an energy company is provided, and then the paper explores why and how this relationship exists in the second stage of the analysis. The mainstream and critical accounting literature is bridged by focusing on issues that are largely the domain of one sub‐literature with a differentiated case study that is largely encouraged in the other.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".