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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 OpenAlex
Hossam Ali‐Hassan, Dorit Nevo, Henry Kim, Stephen Perelgut

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.508
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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

Quick stats

Citations29
Published2011
Admission routes1
Has abstractyes

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