Corporate accountability during crisis in the digitized era
Bibliographic record
Abstract
Purpose Despite an increasing trend in adoption of social media by for-profit organizations and their chief executive officers (CEOs), there is little understanding of how these new channels of communication are incorporated into the broader communication domain of a firm to discharge accountability during a crisis, when accountability is of critical importance. More importantly, research on how people perceive a crisis and voice their opinions to firms and CEOs on social media in reaction to that crisis is rather limited. Therefore, in this study the authors investigate these questions. Design/methodology/approach This study is based on a case. The authors focus on the biggest data breach in Internet history in a pioneer technology firm, the Yahoo data breach. The authors conduct descriptive and dramaturgical analyses informed by Goffman to investigate how Yahoo manages its several front stages (communication channels), including social media during and after the Yahoo data breach announcements, and how people respond to the Yahoo's front stage management. Findings The results show that, during this crisis, Yahoo engages in management of its front stages by first limiting them to a few, then by redrawing the line between its back and front stages, and finally by expanding its front stages to include two-way communication channels, including social media. An ongoing accountability process back stage guides Yahoo's management of its front stages and undermines Yahoo's accountability in front stages. However, social media audiences challenge Yahoo's control of its front stages by using various frames to make sense of the crisis, and to demand accountability. Originality/value This study furthers the understanding of how social media platforms are positioned in a firm's broader communication channels during a crisis. It also enhances understanding of accountability demand, especially during critical times in a digitized era.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".