Entrepreneurial Orientation among Migrants and Small and Medium Enterprises
Bibliographic record
Abstract
Entrepreneurship is a process of creating value by bringing together a unique package of resources to exploit entrepreneurship opportunities. The importance of entrepreneurial activities in society has been acknowledged in the entrepreneurship literature. It has been suggested that entrepreneurial strategies, in terms of entrepreneurial orientation (EO), contribute greatly to entrepreneurship performance (Lumpkin & Dess, 1996; Miller, 1987; Reynolds, 2000). EO is often mentioned as an antecedent of firm growth, competitive advantage, and superior performance. Prior research confirms that a positive relationship exists between EO and firm performance (Wiklund & Shepherd, 2005; Wang, 2008; Runyan, 2008). The success of enterprises are dependent on EO and a range of other factors including age, gender, culture, education, managerial know-how, length of time, size of enterprise, capital, and network connection. Three disciplines have provided primary contributions to the theoretical development of entrepreneurial orientation (EO). These are economics, social psychology, and strategic management ( Mitchell et al., 2002 ). EO refers to the strategy-making processes and styles of a firm that engage in entrepreneurial activities. There are two schools of thought on EO: the Unidimensional School with three dimensions (proactiveness, risk-taking, and innovativeness) and the multidimensional school with five dimensions (innovativeness, risk-taking, autonomy, proactiveness, and competitive aggressiveness). An overview of the entrepreneurial literature shows that there has been little focus on the influence of EO on migrant enterprises. Therefore, the objective of this study will be to examine the relationship between five dimensions of EO and entrepreneurial performance. The study will also assess the moderating influence of environment (environmental characteristics) on the EO-performance relationship.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".