Bibliographic record
Abstract
This study applied body chemistry and neuroscience to leadership behaviour. Specifically, it sought to replicate some studies on the relationship between leadership behaviour and happiness. The research method was exploratory because of the paucity of research on leadership behaviour and happiness and involved the use of qualitative and quantitative techniques. The qualitative technique centred on a review and summarisation of existing literature on leadership behaviour, happiness hormones, and happiness on Google Scholars. The quantitative research used a valid and reliable online questionnaire survey on Google Forms to collect information from 80 respondents on different WhatsApp platforms in Canada, the United States of America and Nigeria, selected by judgemenal non probability sampling technique. They were asked if there was a relationship between leadership behaviour and happiness. Six descriptive data analysis methods- tabulation, bar graphs, mode, summations, percentages and average score per respondent - were applied to answer the research question from the coded questionnaire responses. It was found out that there was a relationship between leadership behaviour and happiness. The research also identified the main hormones and neurotransmitters, secreted by mostly the brain, that increase the wellbeing and happiness of leaders and other persons. They are dopamine, oxytocin, serotonin and endorphins (DOSE). It is recommended that leaders should initiate happiness generating activities like kindness, appreciation, team work, praising others, outdoor exercises, get- togethers and handshakes to enhance their leadership behaviours. The researcher also recommended that leadership recruitment, selection, training, evaluation and rewards should be centred on the capacity to generate and distribute happiness. Leaders should be ‘happifiers’ or happiness generators and distributors. Keywords: Body chemistry, leadership behaviour, happiness, dopamine, oxytocin, serotonin, endorphins and happiness.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".