Ideology, Leaders and Employee Behaviour: An Integration of Transformational Leadership Theory
Bibliographic record
Abstract
Myriad of literature and studies on ideology articulate it as a concept and theory elaborating on its formation process, attributions and connotations; laying emphasis on it as a social or political science concept without inclining it to employee behaviour in businesses. This paper investigates the role of ideologies of leaders in their decision making and its effect on employee behaviour, focusing on entrepreneurs of SMEs and their employees in Ghana. The study adopts the quantitative approach using structured questionnaires as the main data collection tool. Considering conservatism and liberalism as the main forms of leadership ideologies, the transformational leadership theory is used as the lens to explain the authors’ synthesised concept of leadership ideologies and employee behaviour. A regression analysis, utilising the tolerance statistical model, analysis of variance (ANOVA) and the variance inflation factor (VIF) is used to assess the multicollinearity (correlations) that exists between the variables. The coefficients of correlation reveal a positive relationship between leadership ideologies and employee behaviour. The study establishes that employee behaviour, to a limited degree is determined by the ideologies held by leaders. The researchers recommend liberalism as a more favourable leadership ideology for encouraging the realization of individual potentials of employees, enhancing creativity and cohesiveness at the workplace such as attained under transformational 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".