The Power to Reward vs. the Power to Punish: The Influence of Power Framing on Individual-Level Exploration
Bibliographic record
Abstract
This article adopts a relational perspective to demonstrate that characteristics of the dyadic relationship between supervisors and their employees are critical to understanding individual-level exploration—understood as the extent to which organizational members pursue new opportunities and experiment with changes to current practices. To this end, we introduce the concept of power framing—that is, whether the control over valued resources is emphasized as the ability to reward or to punish—and propose that supervisor power framing shapes employee exploration. In an experimental study, we demonstrate that reward (versus punishment) power framing increases employee exploration behavior and that this effect is mediated by perceived trustworthiness of the supervisor. In a second survey study, we replicate these findings in a field sample and show that the relationship between reward power framing and exploration depends on the degree to which the focal employee is sensitive to power characteristics (i.e., power distance orientation). This investigation advances scholarship on the microfoundations of exploration while also highlighting the ability of leaders to alter trustworthiness perceptions and induce employee exploration through power framing. Funding: This work was supported by a National Science Foundation CAREER Award from the Directorate for Social, Behavioral and Economic Sciences [Grant 1943688] granted to O. Schilke. Additional funding was provided by the Sauder School of Business, University of British Columbia. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.1672 .
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 | 0.004 | 0.025 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".