Measuring the Effects of Relationship Quality and Mutual Trust on Degree of Inter-Firm Technology Transfer in International Joint Venture
Bibliographic record
Abstract
The success of technology transfer (TT) within international joint ventures (IJVs) in the developing countries has frequently been measured by the degree of technology that is transferred to local partners. As compared to other formal technology transfer agents such as foreign direct investments (FDIs) and licensing, technology transfer through IJVs have been acknowledged by many studies as the most efficient mechanism to internalize the foreign partner’s technologies, knowledge and skills which are organizationally embedded. However, the transfer process has always involved a complex relationship between IJV partners which may cause direct impact on degree of technology transfer. The success of inter-firm TT requires a strong existence of a close and intense communications between the technology supplier and recipient. The main objective of this paper is to empirically examine the effects of two critical elements of relationship characteristics: relationship quality and mutual trust on two dimensions of degree of technology transfer: degree of tacit and explicit knowledge. Using the quantitative analytical approach, the theoretical model and hypotheses in this study were tested based on empirical data gathered from 128 joint venture companies registered with the Registrar of Companies of Malaysia (ROC). Data obtained from the survey questionnaires were analyzed using the correlation coefficients and multiple linear regressions. The results revealed that relationship quality, as the critical element of relationship characteristics, has a significant effect on both degrees of tacit and explicit knowledge; where the effect was slightly stronger on degree of explicit knowledge. Similarly, mutual trust between partners has shown consistent strong significant effects on both degrees of tacit and explicit knowledge; where its effect on degree of tacit knowledge was found slightly stronger than degree of explicit knowledge. The study has bridged the literature gaps in such that it offers empirical evidence on the effects of two generic relationship attributes: relationship quality and mutual trust on two dimensions of degree of inter-firm technology transfer: degree of tacit and explicit knowledge in IJVs.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".