Aligning personal and entrepreneurial vision for success
Bibliographic record
Abstract
Purpose While there is no definitive profile of the successful entrepreneur or prescribed pathway for success, research suggests that individuals who proactively accommodate factors that push and pull them into entrepreneurship, align their personal and entrepreneurial visions, and to some extent, build emotional intelligence (EQ), are more likely to succeed. This paper aims to describe an entrepreneur counseling process developed and used by the Acadia Centre for Social and Business Entrepreneurship (ACSBE), located in Nova Scotia, Canada. Design/methodology/approach The authors propose an entrepreneur's success, negotiation of push and pull factors, and EQ are all linked, and the ACSBE counseling model draws on these. The case study method was used. ACSBE staffs were interviewed regarding the entrepreneur counseling process, counselor‐training sessions were observed and documents were reviewed. Two ACSBE clients, who together started a successful fair‐trade business, were interviewed for their insights regarding the ACSBE counseling model and their own experiences starting their business. Findings The responses of the ACSBE clients illustrate a successful application of the ACSBE Entrepreneurial Decision Making Cycle©. Their personal values, business strategies and performance were linked to promote success personally and for society. Both entrepreneurs were authentic, self‐aware and empathetic individuals who were able to hone their EQ and develop sound business acumen with assistance of the ACSBE counseling model. Research limitations/implications The analysis of the ACSBE counseling model and its success in this case leads to the question of whether the application of the ACSBE Entrepreneurial Decision Making Cycle can predict those more likely to succeed in an entrepreneurial venture. In order to address this, further research of the ACSBE decision tool is recommended. Originality/value The ACSBE Entrepreneurial Decision Making Cycle is unique. It should be of interest to entrepreneur counselors and researchers of entrepreneurship.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.004 |
| 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".