Acquisition of Knowledge in Networking for Internationalisation
Bibliographic record
Abstract
Abstract Business networks are of critical importance to firms and essential to the internationalisation of born-global and international new venture firms. Networking literature focuses on what are, generally, co-operative relationships and their effects between actors, activities and resources and illustrate the importance of quality and change in the networking process. Utilising Fletcher and Harris’ (2012) framework for understanding knowledge acquisition processes in internationalisation, this study investigates the importance of direct and indirect roles played by third parties in the networking for internationalisation processes of three different firm types within the knowledge-based natural health products (NHPs) (pharmaceutical) sector in Canada. The research presented here examines nine case studies of Canadian NHP firms and reveals that they utilised all network-related internationalisation processes simultaneously to internationalise including Johanson and Mattsson’s (1988, 1994) network theory, Johanson and Vahlne’s (2003) updated the Uppsala Model and the resource-based perspective on network theory (Ruzzier et al., 2006). They networked with and extensively utilised third parties, including government bodies, trade associations, government advisors, consultants and other domestic networks with international ties, in Canada and internationally to gain technical, market and internationalisation knowledge, and direct and indirect experiential knowledge which contributed to the internationalisation process confirming the study by Fletcher and Harris (2012). In a departure from the literature, this study found that weak ties (Granovetter, 1973) developed with third parties who were new to the networks allowed the NHP firms to develop competitive advantages necessary for them to overcome the liability of outsidership in entering new international markets. The type of technical, market and internationalisation knowledge gained, its content and the direct and indirect sources of knowledge from third parties were all shown to contribute to the internationalisation process.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".