Developing positive self-leadership through “Inner Engineering”
Bibliographic record
Abstract
Purpose The study evaluated the effect of a comprehensive yogic methodology called “Inner Engineering Online” (IEO) on developing positive self-leadership. The authors hypothesized that IEO would equip participants with knowledge and skills to optimize their functioning in major experiential dimensions of the self (body, mind, emotion, and energy) and produce a synergistic effect in enhancing well-being and positive organizational behavior for employees, leaders, and entrepreneurs. Design/methodology/approach The study uses a field quasi-experimental one group design with pre- and post-tests. The sample consists of 97 employees, 84 leaders and 76 entrepreneurs in various industries (N = 264). Findings The pairwise t-test results show that IEO has a positive effect on well-being (mindfulness, joy, vitality, and restfulness) and positive organizational behavior (meaningful work, psychological capital, and work engagement). Research limitations/implications The study is limited by the lack of a control group. Future research may use a randomized control design to confirm the present findings and explore the mechanisms through which IEO exercises its effect and other positive outcomes. Practical implications IEO complements the behavioral and cognitive strategies of self-leadership by including emotional and energetic strategies to produce a synergistic effect on positive outcomes. The program is multi-lingual and scalable and can be implemented in and outside of the organizational settings globally. Originality/value The study proposes the concept of positive self-leadership and is the first study to investigate the potentiality of an emerging program for developing positive self-leadership.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".