Knowledge management processes in growth-oriented SMEs : an Atlantic Canadian perspective
Bibliographic record
Abstract
ABSTRACT \nCompetitive pressure and desire for success drive enterprises in general to involve in knowledge acquisition and dissemination activities that are becoming increasingly significant in the rapid changing and globalising economic world. \n \nIn addition, with the increased mobility of information and the global labour force, knowledge and experience can be transferred instantaneously around the globe; thus, any advantage gained by one company can be eliminated by comparative improvements overnight. Therefore, the only comparative advantage a particular company will face will be its process of innovation – combining market and technology know-how with the resourceful talents of knowledgeable labour to solve a constant stream of competitive problems- and its ability to derive value from information. In this context, internal and external knowledge acquisition, intra-firm knowledge dissemination and management decisions taken in response to the significant information generated and subsequently filtered became the key factors of entrepreneurial success. \nThis thesis explores how market orientation, learning orientation and entrepreneurial orientation systematically contribute to and are sources of competitive advantage in growth-oriented SMEs. The objective of this study was to investigate the likelihood of a growth-oriented enterprise established in Atlantic Canada to be involved in knowledge acquisition and dissemination activities and to succeed conditional on numerous internal and external factors. \n \nA ‘mixed-methods’ research approach was used in this study, comprised of: 1) a web-based questionnaire to study the knowledge management process and other aspects of entrepreneurial success and 2) ‘semi-structured’ interviews with a sample of the responding entrepreneurs. \n \nThe findings suggest that knowledge management practices: external acquisition, intra-firm dissemination and responsiveness, do vary across the levels of entrepreneurial performance among the Atlantic Canadian SMEs investigated in the study. Having a market orientation and investing in human resources of the firm were found to be critical drivers of innovation leading to potential competitive advantage.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".