MétaCan
Menu
Back to cohort
Record W2014547908 · doi:10.1504/jgba.2009.023098

Impact of formal control mechanisms on the performance of international joint ventures

2009· article· en· W2014547908 on OpenAlexaff
Marcela Porporato

Bibliographic record

VenueJ for Global Business Advancement · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsYork University
Fundersnot available
KeywordsCorporate governanceBusinessControl (management)InternationalizationMechanism (biology)Joint (building)Key (lock)International joint ventureIndustrial organizationManagement control systemTransfer pricingJoint ventureEconomicsComputer scienceFinanceManagementInternational tradeComputer securityMultinational corporationCommerce

Abstract

fetched live from OpenAlex

Joint ventures (JV) are often a key component of the internationalisation strategies of companies. However, such operations are successful less often than expected. Problems rooted in the management accounting systems (MAS) are mentioned as typical failure causes (Watson Wyatt, 2000). This paper contributes to the understanding of JVs control mechanisms, MAS in particular, by considering them as coordination and monitoring mechanisms (Davila and Foster, 2005). The evidence, from 65 surveyed JVs and three JVs studied in detail in the motor/auto parts industry, supports the theory that highly intensive use of control mechanisms reduces uncertainty (Davila, 2000). This study demonstrates that control mechanisms assume either a coordinating role (budgeting, transfer pricing and cost allocation) or a monitoring role (performance measurement and governance mechanism). Successful international JVs predominantly adopt a balanced use of MAS, but giving pre-eminence to the coordination role as a manner to reduce uncertainty in decision making without eroding trust. However, monitoring roles are also needed to avoid opportunistic behaviours.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.235
Teacher spread0.227 · 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 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

Citations4
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJ for Global Business AdvancementSame topicAccounting and Organizational ManagementFrench-language works237,207