Disentangling empowerment: Considering the role of employee perceptions in adaptive selling
Bibliographic record
Abstract
Abstract As more jobs require employees to continually learn and adapt, empowering employees to adopt learning goals becomes a competitive advantage. Although research has identified key leadership behaviors that empower employees, the relationship between managers and empowered employees is ongoing and dynamic. Therefore, it is important to understand how key leadership behaviors interact with employees in their current state of empowerment to influence learning and performance. In this paper, we draw from self‐determination theory to argue that the way employees perceive a given leadership behavior will differ by dimension of empowerment, and these perceptions impact their intrinsic motivation to learn. Specifically, employees who are high in competence and impact will perceive the leadership behavior of goal clarity as controlling, and be less likely to set learning goals, while those who are high in autonomy and meaning will perceive the same behavior as supportive, and be more likely to set learning goals. In a time‐lagged study of 349 salespeople, we find that goal clarity acts as a boundary condition by weakening the relationship between empowerment and learning goal orientation (LGO) for those high on the competence and impact dimensions. As hypothesized, we find that the same behavior of goal clarity actually strengthens the relationship for those high on the autonomy and meaning dimensions. Further, we provide theoretical and empirical evidence that LGO mediates the relationship between empowerment and adaptive selling.
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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 | 0.003 | 0.010 |
| 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 | 0.002 |
| 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".