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Record W4411310997 · doi:10.1111/1911-3846.13056

Organizational altercasting: Developing impression management and cyber‐risk disclosures

2025· article· en· W4411310997 on OpenAlexfundvenueno aff
Monika Łada, Alina Kozarkiewicz, Jim Haslam, Agnieszka Kabalska, Frank Mueller

Bibliographic record

VenueContemporary Accounting Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
FundersChartered Professional Accountants of Canada
KeywordsImpression managementImpressionBusinessPsychologySocial psychology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.005
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.075
GPT teacher head0.384
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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