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Record W2782588388 · doi:10.1111/isj.12181

IT‐mediated social interactions and knowledge sharing: Role of competence‐based trust and background heterogeneity

2018· article· en· W2782588388 on OpenAlex

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

VenueInformation Systems Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsKnowledge sharingKnowledge managementCompetence (human resources)Social competenceSocial knowledgeInformation sharingBusinessSocial mediaSocial relationPsychologySocial psychologyComputer scienceSociologySocial changePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract In the knowledge‐based economy, organizational success is dependent on how effectively organizational employees share information. Many studies have investigated how different types of communication activities and communications media influence knowledge sharing. We contribute to this literature by examining increasingly prevalent yet understudied IT‐mediated social interactions and their effects on knowledge sharing among employees in comparison to face‐to‐face social connections. By integrating the literature on knowledge sharing, social networks, and information systems, we theorize the ability of IT‐mediated social interaction to (1) afford interactions between individuals with heterogeneous backgrounds and (2) facilitate frequent IT‐mediated social interactions that are high in competence‐based trust—both supporting effective sharing of knowledge. Through a social network analysis of the employees in a high‐tech organization, this study finds that IT‐mediated frequent social interactions are the most effective in promoting knowledge sharing.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.065
GPT teacher head0.349
Teacher spread0.285 · 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