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Record W4361222038 · doi:10.1080/14778238.2023.2193347

Organizational enablers and outcomes of IT affordance actualisation: a socio-technical perspective on knowledge sharing

2023· article· en· W4361222038 on OpenAlexaff
Marie Christine Roy, Mustapha Cheikh‐Ammar, Marie‐Josée Roy

Bibliographic record

VenueKnowledge Management Research & Practice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAffordanceKnowledge sharingKnowledge managementPerspective (graphical)BusinessGovernment (linguistics)PsychologyComputer science

Abstract

fetched live from OpenAlex

Using the IT affordance lens, this study presents a holistic socio-technical view of the organisational factors that affect the knowledge-sharing (KS) behaviour of employees. It develops and empirically validates a conceptual model that theorises the relationships between KS affordances, organisational culture, management support, and peer KS in order to specify how these relationships shape the KS behaviour of employees. Data for this study was collected using a survey of a large pool of public service employees working at various government agencies (n = 4,090 respondents). The results of this study provide evidence that KS affordances offered by both traditional KS information systems and by enterprise social media increase employees’ overall willingness to share their knowledge. However, these results also show that KS affordances are more likely to be perceived when organisational culture favours KS, and that management support moderates the relationship between KS affordances and employees’ KS behaviour.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.006
Scholarly communication0.0060.005
Open science0.0010.006
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 head0.128
GPT teacher head0.458
Teacher spread0.331 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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