Is There a Time and Place for Transformational Leadership? \nThe Daily Impact of Leader Behaviors on Follower Task Performance
Bibliographic record
Abstract
The purpose of this study was to weigh the value of Transformational Leadership (TFL) and Contingent-Reward Leadership (CRL) behaviors in determining followers' objective task performance. Past empirical evidence has suggested that TFL behaviors were relatively less important for influencing individual task performance in comparison to CRL behaviors. This study was designed to test the boundaries of this empirical conclusion. There were two main goals: 1) to investigate the daily effects of TFL and CRL behaviors on follower task performance given a "chaotic" work context, and 2) to address a temporal issue related to the measurement of TFL and its influence on individual task performance. Daily data were collected from a sample of 46 Canadian tree planters over a period of nine work days. The sample was composed of novice and experienced personnel. The data were hierarchically cross-classified, with days nested within individuals, and individuals nested between leaders. Hierarchical multiple regression and PROCESS moderation analysis results point to short-term effects of CRL and long-term effects of TFL for influencing task performance in both groups. Interestingly, the vision item from the inspirational motivation dimension of TFL demonstrated both a significant daily and lingering association to the task performance of novice and experienced tree planters. Implications of the results for leadership theory and practice are discussed, along with a review of the study strengths and limitations. To conclude, the author offers suggestions for the direction and focus of future leadership research given the changing nature of 21st century work environments.
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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 | 0.000 | 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 | 0.000 |
| 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".