Technology Transfer Potential in Local and Foreign‐Owned Firms in Emerging Economies
Bibliographic record
Abstract
Abstract Technology transfer in international collaborations is challenging but can bring benefits to both local and foreign‐owned firms in emerging economies. In this paper we focus on conditions for potential technology transfer in emerging economies. We develop a configurational theoretical framework and empirically operationalize it using qualitative comparative analysis. Building on differences in absorptive capacity between these two kinds of firms and relying on data from the construction industry in Ghana, we develop a process model of technology transfer in emerging economies. Our model shows that technology transfer in local and foreign firms can be achieved through different combinations of human resource development and knowledge management, as well as international collaborations and networks. The model also explicates mechanisms leading to potential technology transfer. Based on the findings and the process model, the study makes several contributions to the absorptive capacity and technology transfer literature in emerging economies by shedding light on the underlying processes that foster a firm's ability to absorb technology in international collaborations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".