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Record W2062048695 · doi:10.5267/j.ijiec.2010.01.008

The challenges of knowledge management portals application and implementation:An Iranian organizations case study

2010· article· en· W2062048695 on OpenAlexvenueno aff
Peyman Akhavan, Mohammad Saleh Adalati, Sara Sharifi, Reza Hosnavi Atashgah

Bibliographic record

VenueInternational Journal of Industrial Engineering Computations · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge managementBusinessSuccess factorsComputer scienceBusiness administration

Abstract

fetched live from OpenAlex

This article aims to review, identify and prioritize challenge factors of the implementation of knowledge management portals for Iranian organizations. The study determines several important weakness factors affecting the implications of the knowledge management such as the weakness in organizational strategy, information overcrowd, content management, portals project management, and etc. The study also indicates that the factors have different priorities where managerial factors are in the highest priority and financial factors are in the lowest priority. We also perform factor analysis to summarize seventeen factors into six issues: Financial and information security, Technology and management, Senior management support and strategy, Acceptance, User's motivation and culture, Project management, Change management and training. Knowledge management portal is a brand new concept for Iranian organizations and it is hard to gather information from limited number of organizations or companies for applied knowledge management portals. The results of this study could be useful for knowledge management planners and managers in organizations and clear the prospects to deal with the challenges.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.267

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.037
GPT teacher head0.353
Teacher spread0.316 · 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 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

Citations5
Published2010
Admission routes1
Has abstractyes

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