Entrepreneurial Growth Aspirations and Familiarity with Economic Development Organizations: Evidence from Canadian Firms
Bibliographic record
Abstract
The purpose of this paper is to investigate the relationship between the entrepreneurship ecosystem and the entrepreneur’s willingness to grow. This study is particularly interested in exploring the relationship between entrepreneur’s familiarity with the key economic development organizations in the entrepreneurship ecosystem and the willingness to grow. Several studies have investigated the growth process in small and medium sized enterprises (SMEs) since the case has been made that high growth SMEs contribute to economic growth through job creation. To date, these studies have identified multiple internal and external determinants including their effects on small business growth. There is evidence in the literature that characteristics of the entrepreneurs such as the willingness to grow and the entrepreneur’s network are important factors in growth process. However, the relationship between growth process and the entrepreneur’s networking behavior is yet to be fully understood. Drawing from the entrepreneurship ecosystem literature, the growth process literature and the resource dependence theory, this study uses the business confidence survey from 2011 to 2013, which targeted all businesses across all of Halifax Regional Municipality (HRM) in Nova Scotia, Canada, to explore the relationship between the entrepreneur willingness to grow and the propensity to network with key economic development organizations of the entrepreneurial ecosystem. The findings support the assumption that the proportion of businesses that are willing to grow (i.e. hire additional staff and enter new markets within the next twelve months) is higher for the group of businesses that are familiar with the key economic development organizations than for the group of businesses that are not familiar with them. However, the results are not homogeneous across all populations. Our findings also indicate that the higher the expectation to enter new markets over the next twelve months, the higher the odds to be familiar with the key economic development organizations. Our findings contribute to the literature around the association between networking and small business growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".