Exploring the Factors Affecting the ICT Technology Transfer Process: An Empirical Study in Libya
Bibliographic record
Abstract
The purpose of this work is to develop a model that describes the international technology transfer (ITT) process of information and communication technology (ICT) from developed to developing countries. This paper is a part of an ongoing study aiming to develop a technology transfer model examining the embracing of a foreign advanced technology to the ICT companies as well as ICT-based SME’s projects in Libya. The past relevant TT models are reviewed with the intention of exploring and sort out the most ITT influential factors. The questionnaire that conducted recently in the TT process in the Libyan ICT industry was utilized to verify the model. Major statistical techniques are applied to analyze the survey received data. To establish reliable measures for the factors and sub-factors under investigation as well as to reduce their numbers, Exploratory Factor Analysis (EFA) was implemented. Furthermore, the goal behind using (EFA) is to combine these sub factors according to a theoretical conceptual. Several sub factors and items were dropped and the model’s factors were regrouped as TT government support initiatives, transferor characteristics, transferee characteristics, TT environment, and learning centers. In addition, the TT outcome (achievements) factors, are identified and refined, some items were discarded. The outcome of this analysis is the verified model for ITT in ICT projects, which includes a number of refined enabling and achievements variables.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".