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Record W2923882886 · doi:10.17722/ijme.v12i2.1064

Determinants of Corporate Reputation towards Knowledge Sharing

2019· article· en· W2923882886 on OpenAlexvenueno aff
Romel C. Nemino

Bibliographic record

VenueInternational Journal of Management Excellence · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
Fundersnot available
KeywordsReputationKnowledge sharingBusinessStratified samplingEmpirical researchCorporate communicationMarketingAccountingKnowledge managementCorporate governanceFinanceComputer science

Abstract

fetched live from OpenAlex

This paper examines the underlying relationship between corporate reputation and knowledge sharing of commercial bank employees in Caraga Region, Philippines. Its objective is to determine the levels of corporate reputation and knowledge sharing. In the same vein, correlation measures using the Pearson Product Moment Correlation between corporate reputation on knowledge sharing is also explored. Using the stratified random sampling technique, 400 bank employees across the region are identified as the primary respondents of this empirical research. As perceived by the respondents, findings reveal a very high level of corporate reputation and knowledge sharing among the bank employees. Moreover, strong evidence on the positive relationship between the constructs corporate reputation and knowledge sharing is uncovered. Among the dimensions of corporate reputation, corporate communication revealed the highest r-value when correlated with knowledge sharing. The findings of this study substantiate an empirical contribution not only to the banking industries but to other business organizations making this proposed research model as the laying ground for future policy reviews and formulations to improve business performance.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.048
GPT teacher head0.343
Teacher spread0.295 · 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 designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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