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The Role of Enterprise System Implementation in International Joint Venture Development: Exploring the Relationship

2004· article· en· W1722058596 on OpenAlexaff
James Ding

Bibliographic record

VenueThe Electronic Journal of Information Systems in Developing Countries · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInternational joint ventureConstruct (python library)BusinessJoint ventureJoint (building)Value (mathematics)Enterprise systemInvestment (military)Process managementKnowledge managementIndustrial organizationOperations managementComputer scienceEconomicsPolitical scienceBusiness administrationEngineering

Abstract

fetched live from OpenAlex

Abstract In recent years, the rapid development in International Joint Ventures (IJV) has been a significant phenomenon under the global economy, especially in the developing countries. However, high failure rates and hence the huge investment risks make the promised advantages unpredictable. In this study, we explore the role of Enterprise System (ES) implementation in the journey of IJV development. We study the impacts of ES to IJV through a comparison study for their relationship that involves three scenarios: implementing ES prior to, parallel to, or after IJV operation. We then construct a generic framework for the optimal strategy based on findings from both ES and IJV literature. We also use a comparative case study approach to illustrate the value of this framework. We conclude our study with some discussions on possible extensions to innovation management as future research directions.

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)0.010
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.004
Scholarly communication0.0090.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.021
GPT teacher head0.262
Teacher spread0.241 · 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 designObservational
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

Citations0
Published2004
Admission routes1
Has abstractyes

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