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Record W2063543614 · doi:10.1142/s0219649212500104

Tacit Knowledge Sharing During ERP Acquisition: An Exploratory Multi-Site Case Study

2012· article· en· W2063543614 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Information & Knowledge Management · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsTacit knowledgeKnowledge managementEnterprise resource planningNegotiationKnowledge sharingProcess (computing)Knowledge acquisitionBusinessComputer scienceExplicit knowledgeExploratory researchProcess management

Abstract

fetched live from OpenAlex

Organisations that implement Enterprise Resources Planning (ERP) software packages are making a big commitment in terms of both time and money. Realising the ERP benefits, some organisations have successfully implemented while others have struggled, settled for minimum returns, and abandoned the system. To mitigate the risks, a knowledge sharing framework is suggested to be put in place during ERP acquisition. Based on findings in an explorative case study of three Canadian organisations that have gone through ERP acquisition phases, this study examines tacit knowledge sharing in ERP acquisition planning process, information search process, selection, evaluation, choice, and negotiations. The lessons learned and knowledge sharing activities are given by presenting a cross-comparison of the case studies.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.021
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.055
GPT teacher head0.323
Teacher spread0.268 · 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