Transformational Leadership and Work Engagement in the Automotive Retail Industry: A Study of South Africa
Bibliographic record
Abstract
Real leadership is needed in the automotive industry’s competitive environment to guide subordinates so that they share goals, attitudes, values, and work towards the achievement of organisational strategies. Macroenvironmental changes such as the slowdown in the South African economy, labour unrest, high unemployment levels, a weakening currency, and new vehicle price increases have had a detrimental effect on automotive retailers and can be blamed partially for dealers struggling to reach targets in recent years. This perpetually fluctuating external environment promotes corresponding internal automotive dealership changes and strategies. This might mean changes to intangible resources like dealership processes, policies, procedures, or physical resources like people, demographics, materials and products. In both cases, strong leadership is required. The primary aim of this exploratory study was to determine whether sales managers exhibited a predominately transactional or transformational leadership style, and to understand current levels of work engagement of sales executives in motor dealerships’ new and used vehicle sales departments. A secondary aim was to examine the correlation between the prevailing leadership style (either transactional or transformational) of sales managers and the level of work engagement of sales executives. The research method included a formal quantitative, cross-sectional survey. Data was collected using questionnaires developed by international researchers in the field of transformational and transactional leadership and work engagement. The main findings of this research will contribute to current literature and knowledge relating to work engagement and its interdependence with transformational and transactional leadership.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".