Business Plan Vs Business Model Canvas in Entrepreneurship Trainings: A Comparison of Students’ Perceptions
Bibliographic record
Abstract
Business model canvas and business plan are prevalent and widespread tools used in entrepreneurship trainings. This study aims to compare the business model canvas and the business plan as tools used in entrepreneurship trainings through the perceptions of business school students, with a user approach. Students were given applied entrepreneurship courses, and have been taught to prepare a business plan and a business model canvas. Then students were asked questions comparing business plan and business model canvas from various aspects. 62% of the students have stated that they find it more difficult to prepare a business plan. On the other hand despite its hardship to prepare, students have stated business model's superiority to BMC on several issues. According to students' statements, compared to BMC, business plan is more clear (64.6%), more useful (60.8%), more realistic in revealing the phases of start-up (77.2%), superior in financial planning (74,7%), superior in marketing planning (67.1%), superior in costs (70.9%), superior in describing customer needs and value propositon (60.7%), superior in production planning and supply chain (68.3%). After providing these statements students were asked which system they liked preparing the most. Answers to this question could not be decisively evaluated. Percentage of positive, negative and neutral statements are very similar. Independent samples t-test was conducted to compare business plan and BMC use perceptions scoring for gender. There was not a significant difference in the scores for female and male students.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".