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Record W3125912227 · doi:10.15173/mjc.v12i1.2374

Internal communications and goal achievement: The CEO’s perspective

2020· article· en· W3125912227 on OpenAlexaffvenue
Rita Chen

Bibliographic record

VenueThe McMaster Journal of Communication · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInternal communicationsGroup cohesivenessPublic relationsReputationLoyaltyBusinessExcellenceFunction (biology)Perspective (graphical)ManagementMarketingPsychologyPolitical scienceComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

By effectively utilizing internal communications, CEOs are able to influence organizational culture and communications, inspire employee loyalty and engagement, and build brand image at both company and personal levels. In fact, some scholars believe that the CEO is more responsible for fostering forthright, transparent internal communications than the organization’s actual communications function. CEOs who are successful in promoting internal communications can positively influence organizational stakeholder relationships and better achieve their strategic goals. Through interviews with five CEOs, this paper determined that two-way internal communications was regarded by senior leadership as being necessary for organizational cohesiveness, strategy development, strategic reputation management, boundary spanning, and preemptive problem prevention. The CEOs interviewed also considered it their responsibility to model and nurture internal communications and regarded the function as contributing to the achievement of their organization’s strategic goals. Keywords: internal communications, IABC Excellence Theory, CEO, senior leadership, goal achievement

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.335
Teacher spread0.286 · 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 designQualitative
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

Citations3
Published2020
Admission routes2
Has abstractyes

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