Bibliographic record
Abstract
Purpose The purpose of this paper is to describe the timing of management control systems (MCS) implementations, their drivers and effect on joint venture (JV) survival. Design/methodology/approach This paper draws on case study data (archival data, interviews, and site visits) collected at three JVs in the automotive industry. Contingency theory is used to define Cartesian relationships. Findings A description of the timing and reasons for MCS implementation in JVs is provided. Initially, environment, strategy, and partner culture are considered to implement governance mechanisms and transfer prices/cost allocations for long‐term transfers of technology and corporate services. Later, structural and technological factors are considered to implement operative MCS such as budgeting, transfer prices/cost allocations of manufactured parts and performance measurement. Research limitations/implications All three JVs studied: belong to the automotive industry (SIC 3174); have balanced ownership (50/50); and have one partner in common (a European family‐owned business with professional management). Data are obtained mainly through site visits, five interviews, five mailed questionnaires, and public and private archival data. Originality/value The paper is the first to offer a descriptive model of the timing of MCS implementation in 50/50 JVs explained by the effect of contingent factors in each stage of the JV life and in JV survival.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".