Student Leadership in Volatile, Uncertain, Chaotic, and Ambiguous Business Environment: Experiment from the Complexity Leadership Theory
Bibliographic record
Abstract
Purpose: Student leadership has become an increasingly critical issue over time. Despite the progress in some areas, the Volatile, Uncertain, Chaotic, and Ambiguous (VUCA) world still significantly hinders students' ability to lead in social, economic, and political spheres. Methods: An explanatory research design was employed in this study. Convenience sampling is used to select participants, and a self-administered questionnaire is utilized. The questionnaire has been modified to align with the study's objectives. Data collection is conducted using the Kobo Toolbox in the Kathmandu Valley, and data analysis is performed using SmartPLS 4.0, which applies both descriptive and inferential data analysis techniques. Findings: The findings indicate that most respondents are aware of and knowledgeable about VUCA. In the VUCA business environment, managing people and delegating work are considered significant challenges. The study identifies major solutions to address these issues, including providing hands-on experience learning opportunities and emphasizing the importance of soft skills. The research reveals that enabling, operational, and entrepreneurial leadership influence VUCA skills, benefiting researchers, future students, educational institutions, governments, and other business organizations. Conclusion: The findings highlight that most respondents are familiar with VUCA and recognize managing people and delegation as the key challenges. The study suggests that enhancing VUCA skills through hands-on experience, soft skills training, and effective leadership can benefit various stakeholders, including researchers, students, and organizations. Keywords: VUCA, Student leadership, Complexity Leadership Theory, SEM JEL Classification: C21, C83
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".