Understanding Communication Interdependencies in Corporate Sustainability Discourse
Bibliographic record
Abstract
This study investigates the interdependent nature of corporate communication in sustainability discourse on social media. Using digital trace data from S&P 500 firms' Twitter accounts (2015–2021), we examine how corporations engage in sustainability communication not in isolation, but through patterned and interconnected behaviors shaped by institutional dynamics. We employ the Generalized Connectedness Approach to quantify sector-level interdependencies, revealing strong cross-sector influence in sustainability posting behavior. Based on network position, environmental business orientation, and responsiveness to public attention, we identify four emergent groups of corporations. We then leverage algorithm-supported induction to elicit feature importance, revealing that patterns of sustainability posts, sociopolitical posting, ESG scores, and social media visibility are key predictors of corporate group (i.e., identified in the previous step) membership. We situate these findings within the theoretical lexicon of mimetic isomorphism and organizational routines, arguing that corporate sustainability discourse is shaped by routinized and mimetic communication practices.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".