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Record W2164599348 · doi:10.1109/hicss.2011.338

Organizational Social Computing and Employee Job Performance: The Knowledge Access Route

2011· article· en· W2164599348 on OpenAlexaff
Hossam Ali‐Hassan, Dorit Nevo, Henry Kim, Stephen Perelgut

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsYork UniversityDalhousie University
Fundersnot available
KeywordsKnowledge managementKey (lock)Knowledge sharingComputer scienceEmpirical researchSocial computingJob performancePsychologyJob satisfactionSocial mediaSocial psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

This paper presents an empirical study investigating the impact of organizational social computing on employees' innovative and in-role job performance. Specifically, we suggest that two key uses of social computing, representing the use of social computing tools for maintaining social relations and for generating and sharing content, are positively related to employees' access to knowledge. Access to knowledge, in the form of expertise location and access to codified information, is in turn positively associated with employees' innovative and in-role job performance. For this study a conceptual model is developed and tested via a cross-sectional survey. The findings suggest that the two key uses of organizational social computing are positively associated with access to knowledge which in turn is positively related to the two forms of job performance at varying degrees. Theoretical and practical implications are discussed, as are directions for future research.

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)0.011
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 score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.086
GPT teacher head0.333
Teacher spread0.247 · 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

Citations29
Published2011
Admission routes1
Has abstractyes

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