Organizational altercasting: Developing impression management and cyber‐risk disclosures
Bibliographic record
Abstract
Abstract The study develops theorizing of external organizational communications that entail impression management. This includes developing linkages to Goffman's work and a Goffmanian research tradition. Our approach innovatively articulates dimensions of impression management entailing the presentation of others and nuanced practices of what we term organizational altercasting (OAC). Altercasting has been conceptualized in a Goffmanian tradition. OAC, seen as implicated in more developed organizational impression management (OIM), involves an organization constructing for another/others (an audience with whom the organization interacts) a persona that is congruent with the organization's goals. Our theorizing also innovatively draws from Goffmanian insight in a coherently associated way—namely, by appreciating the pervasiveness of interaction rituals, including those that take place in an organizational communication style using today's technology. We suggest that OAC especially tends to entail tact. The empirical focus is a case analysis of a Polish bank (CB) facing challenges of cybersecurity and disclosing/communicating externally on cybersecurity/cyber‐risk. For insight, we address this question: In terms of a developed theorizing of OIM (including OAC), how did the bank respond to external challenges, related to cybersecurity, through public disclosures/communications? A content analysis of types of multimedia, with attention given to context, indicated the importance of the presentation of others. We were drawn to how CB's customers, a key audience, were presented in CB's external communications, highlighting long‐term engagement in, and an increase in the significance of, these communications. For our case, articulation of OIM and the presentation of others was further developed through OAC, with particular attention given to communication style vis‐à‐vis modern technology. Our work promotes OAC's wider applicability, including beyond cyber‐risk disclosures.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".