Measuring of Technological Capabilities in Technology Transfer (TT) Projects
Bibliographic record
Abstract
Technology Transfer (TT) projects involve the cross-boarder transfer of technology with the main purpose of enhancing the local technological capabalities in response to a changing economic environment. Technological capability refers to an organisation’s capacity to deploy, develop and utilise technological resources and integrate them with other complementary resources to supply the differentiated products and services. Technological capability is embodied not only in the employees’ knowledge and skills and the technical system, but also in the managerial system, values and norms. Therefore, the phenomena of TT projects occur at the macro and micro level in organisations. The main problem identified in international TT is the lack of managerial capabilities. The common question is how to measure technological capabilities from the perspective of technology receivers. The objective of this paper is to measure the level of technological capabilities in TT projects. The empirical research was undertaken by means of Case Studies using semi-structured interviews with Human Resource Management officers from six (6) Category of G7 contractor companies in Malaysia. . A total of six organisations (currently engaged in overseas projects) were involved representing 100% response rate. The results were analysed using of NVivo software version 8. The findings revealed that, three components for measuring the level of technological capabilities in TT projects are production performance (i.e., construction cost, time, product quality, safety, productivity, profitability, and client satisfaction); technology utilisation (i.e., labour force and organisation & management); and firm’s/orgaisation’s capability (i.e., tools & equipment, research input and development output). These measurements could be used as a guideline for measuring technological capabilities in TT projects for construction organisations.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".