Empirical Investigation on the Effects of Inter-Firm Technology Transfer Characteristics on Degree of Inter-Firm Technology Transfer: A Holistic Model
Bibliographic record
Abstract
The current issue on inter-firm technology transfer (TT) is centered on the efficiency and effectiveness of the transfer process by the multinationals (MNCs) where the success is strongly associated with degree of technology transferred to local partners. Based on the underlying knowledge-based view (KBV) and organizational learning (OL) perspectives, the main objective of this paper is to empirically examine the effects of four critical technology transfer characteristics: knowledge, technology recipient, technology supplier, and relationship characteristics on two distinct 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 regression analyses. The results revealed that relationship characteristics have the strongest significant effects on both degrees of tacit and explicit knowledge followed by technology supplier and recipient characteristics. Contrary to the study’s prediction, but still consistent with the recent development in literature, knowledge characteristics have only significantly affected degree of explicit knowledge not degree of tacit knowledge. The study has bridged the literature gaps by providing empirical evidence on the effects of four critical technology transfer characteristics: knowledge, technology recipient, technology supplier, and relationship characteristics on two distinct dimensions of degree of inter-firm technology transfer: degree of tacit and explicit knowledge in IJVs in a single model.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".