Media interviews as strategic external communication to maintain legitimacy for sustainability activities
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the thematic content and inclusive language in leaders' media interviews to maintain legitimacy for organizational sustainability activities. Design/methodology/approach An exploratory, qualitative content analysis of 24 organizational leaders' media interviews about environmental sustainability was conducted. Inclusive language (i.e. collective focus terms, collective personal pronouns, and metaphors) and thematic content were analyzed. Findings Legitimacy maintenance entails both describing organizational sustainability activities and conveying, through the use of inclusive language, multiple audiences' connection to the organization. The qualitative content analysis found that leaders discussed both primary and secondary stakeholders. With the exception of the code defending existing practices, leaders consistently highlighted positive sustainability activities of their organizations. The inclusive language analysis found that collective focus terms were used by all the leaders, with the most common term being “everyone.” Collective personal pronouns were found in half the interviews. Metaphors were employed by all leaders; the most common sustainability-related metaphors were journey, structural, personification, military/competition, vision and science. Research limitations/implications The sample is limited to 24 organizations and not representative of all industries. Originality/value While sustainability communication research focuses on annual reports and website and social media content, this study draws attention to a common but under-examined type of strategic external communication: senior organizational leaders' media interviews. To the authors’ knowledge, scholars have not previously considered the possible legitimacy maintenance function of organizational leaders' use of inclusive language and thematic content to address a broad array of stakeholders in their external communication.
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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 | 0.044 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".