ICKM 2024: International Conference on Knowledge Management 2024
Bibliographic record
Abstract
This study explores the use of digital technologies and platforms (DTPs) among immigrant entrepreneurs to acquire knowledge and shape creation and growth of their ventures. Immigrant entrepreneurs face business viability concerns due to small size of ventures, concentration in ethnic enclaves and their focus on limited to low margin or highly competitive sectors. The extant literature illustrates that use of DTPs can shape creation of new business models, products, forms of innovation and allow entrepreneurs to transform their businesses. Yet, entrepreneurs' capabilities, skills, and resources and ethnic, social, or cultural factors limit immigrants’ use of DTPs. Recent literature has presented small businesses as frequently encountering malware attacks, receiving spam emails and fake invoices, and being subjected to phishing, and other forms of cyber threats. Such issues challenge the establishment, innovation, diversification, and growth of immigrant-owned businesses. This is further exacerbated by the recent increase in use of AI. However, existing literature does not examine these issues nor provide insights into AI and immigrants DTPs use or non-use comprehensively. Grounded on mixed embeddedness and knowledge based dynamic capabilities theoretical foundations, this study examines the means, conditions, and context by which immigrant entrepreneurs utilize DTPs to grow and fulfill their entrepreneurial ambitions to enhance their performance. Immigrant-owned businesses require both embedding and knowledge-based capabilities to successfully adopt DTPs. Drawing on a systematic review of the literature and in-depth interviews with immigrant entrepreneurs in Canada, we contribute to development of enhanced theory, practice, and policy in knowledge management, entrepreneurship, and immigrant integration.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.118 | 0.058 |
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".