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Record W161118458

Motivating Knowledge Sharing in Diverse Organizational Contexts: An Argument for Reopening the Intrinsic vs. Extrinsic Debate

2009· article· en· W161118458 on OpenAlexaboutno aff
Michael Hass, Jason Nichols, David Biros, Mark Weiser, Jim Burkman, Joseph Thomas

Bibliographic record

VenueJournal of the Association for Information Systems · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
Fundersnot available
KeywordsGeneralizability theoryArgument (complex analysis)Context (archaeology)Knowledge managementPsychologyKnowledge sharingOrganizational behaviorIntrinsic motivationSocial psychologyPublic relationsPolitical scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

In an effort to assess the generalizability of factors that influence the disposition of knowledge sharing within organizations, this paper replicates a previous study that provided support for intrinsic motivators playing a dominant role in organizational science literature but applies it to a different community. A recent survey of 156 U.S. and Canadian law enforcement, forensic, and information assurance professionals discovered differences in the findings between the community examined in the earlier study and this one and suggest that there is merit in reopening the discussion between intrinsic and extrinsic motivators and the role they play in an organization. Examination of these two studies further suggests that the impact of motivators, both intrinsic and extrinsic, varies with the context of the knowledge management system to which they are applied. This paper then concludes with suggestions on direction of 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 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)0.006
metaresearch head score (Gemma)0.002
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 score0.514
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.026
GPT teacher head0.294
Teacher spread0.267 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2009
Admission routes1
Has abstractyes

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