Striving toward the future: aspiration—performance discrepancies and planned organizational change
Bibliographic record
Abstract
Interest has been growing in understanding how organizations’ aspiration levels affect their planning for future organizational change. Previous research has not specified whether organizations use direct competitors or other comparable organizations as referents for forming their aspirations. In this study, it is argued that organizations form their social aspirations based on two types of interorganizational comparisons: competitive and striving. In competitive comparisons, an organization compares its current performance against that of its current direct competitors. When relative performance is poor, these organizations plan more extensive and more radical change. However, the study shows that organizations that are performing well relative to competitors do not necessarily become inertial, as theory suggests. Rather, organizations engage in striving comparisons by comparing their current performance against the performance of organizations to which they strive to be like in the future. The analyses show that organizations with large striving discrepancies are driven to more extensive and more radical change, even if they are performing well compared to current competitors. The study examined this interplay between competitive and striving discrepancy in explaining organizational change on a sample of 131 AACSB accredited business schools.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".