Understanding eINVs through the lens of prior research in entrepreneurship, international business and international entrepreneurship
Bibliographic record
Abstract
In this chapter we examine the growing phenomenon of internet-based international new ventures, which we label “eINVS,” through the lens of previous research in the fields of entre- preneurship, international business and international entrepreneurship. Our purpose is to iden- tify where these existing bodies of research help us to understand eINVs, and where there are gaps that constitute important questions for future research. We define an eINV by adapting a widely used definition of international new ventures (INV) (Oviatt and McDougall 2005: 5): an eINV is a venture whose business model is enabled by a digital platform and that, from incep- tion, seeks to derive significant competitive advantage from international growth. With a focus explicitly on how extant research helps us understand eINVs, this review differs from that of Reuber and Fischer (2011b), who focus on firm-level internet-related resources that are related to the internationalization of ventures in general; that of Pezderka and Sinkovics (2011), who focus on risk and the online foreign market entry decisions of small and medium-sized enter- prises (SMEs); and that of Chandra and Coviello (2010), who focus on consumers using the internet to pursue international opportunities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".