Firm identity and image: Strategic intent and antecedents to sustainability reporting
Bibliographic record
Abstract
A firm’s strategic intent is often communicated through its vision, mission, and values statements. By linking sustainability with strategic intent (Galpin, Whittington, & Bell, 2015), firms seek to portray to their stakeholders (Ali, Frynas, & Mahmood, 2017; Papoutsi & Sodhi, 2020) that sustainability is a core part of their long-term goal. But there is limited research about whether publicly avowed sustainability messaging matches firms actual conduct reflected in their sustainability reports (Amran, Lee, & Devi, 2014). Content analysis of the vision, mission, and values statements of firms comprising the S&P/TSX composite index in 2020, and regression modelling tested whether firms’ that communicate their corporate social responsibility intentions, sustainable image, and sustainable identity in their vision, mission, and values statements are also more likely to engage in sustainability reporting. We find that firms were more likely to report, and at greater levels, on their sustainable activities when they message their strategic corporate social responsibility (CSR) intent. However, including external stakeholders when messaging about their CSR intent has a greater effect than the inclusion of internal stakeholders suggesting these firms are keener to portray a sustainable image than creating a sustainable identity. This result has implications for the successful implementation of sustainability strategies by these firms
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".