Enterprise education in undergraduate business programmes advances students' negotiating competence and self-confidence
Bibliographic record
Abstract
Purpose Business graduates’ enterprising capability augments their work readiness, transforming them into professionals capable of driving successful outcomes. At the core lie self-confidence and negotiating competence. However, embedding enterprise education and developing assessments to evidence learning is challenging. This study aims to offer a blueprint for establishing enterprise learning in the classroom and investigating the effectiveness of cultivating negotiating competence and self-confidence. Design/methodology/approach Modelled on Kolb’s experiential learning cycle, students engage in in-class and real-life negotiations, assessing self-confidence using a scale founded in Bandura’s self-efficacy theory. Open-ended reflections are also submitted. Quantitative data is analysed through multiple linear regression, while quantitative and qualitative data triangulation substantiates enterprise learning in negotiating competence and self-confidence. Findings Students’ reflections show that low self-confidence poses an initial barrier in negotiations, overcome with successive engagements. Quantitative analysis uncovers response-shift biases, with female and male students overestimating initial self-confidence levels. The gender and difference score type interaction reveals a more pronounced bias among female students starting from a lower baseline than male students, implying a more substantial self-confidence improvement for female students. These findings challenge traditional assumptions about gender differences in negotiations and emphasize the need for nuanced perspectives. Originality/value Enterprising capability is pivotal for business professionals. This study highlights the advancement of negotiating competence and self-confidence. It contributes uniquely to the development of enterprise education pedagogy. Focusing on nuanced gender differences challenges prevailing assumptions, providing a perspective to the discourse on negotiating competence and self-confidence in management training.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".