Entrepreneurial Competencies, Innovation Enablers and Sustainable Competitive Advantage Among Micro Firms Across Cultures: A Comparative Study of Canada and Malaysia
Bibliographic record
Abstract
Recent research on start-up businesses has drawn attention to the central importance of sustainable competitive advantage (SCA) by accelerating the continuous development of entrepreneurial competencies (EC) through bridging the mediating and moderating peer relationships for entrepreneurial success. Such peer relationships among innovation enablers (IE), government support (GS) and competitive intelligence awareness (CIA) are vital in connecting EC to achieve SCA. However, prior studies scantily emphasized the causal linkages between two different contexts, particularly Canada and Malaysia. This study unpacks the black box of peer relationships contributing to EC and sustainable growth. We espoused a deductive approach with a quantitative methodology using SPSS AMOS version 22 for measurements to address the gaps in the entrepreneurship literature. The dataset comprises 750 respondents from micro-sized wholesale, retail and service firms with 1–3 employees, evenly split between Malaysia and Canada. The empirical sample results show that for Canadian entrepreneurs, the direct effect of EC has a positive and significant influence on SCA, but is negatively and significantly associated in the Malaysian context. Moreover, the CIA had a positive and significant moderating effect on the relationship between EC and IE in both Canadian and Malaysian samples. However, the CIA moderating effect on the relationship between EC and SCA is only positive and significant in the Canadian sample and insignificant in the Malaysian context. Lastly, the moderating effect of GS on the relationship between EC and SCA is positive and significant in the Canadian sample and insignificant in the Malaysian sample. These findings also offer practical clarity to the puzzle of how some EC can become the cornerstone of these firms’ sustainable growth by adopting IE in conjunction with CIA and GS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".