What role does generic strategy play in how managers adapt their aspirations in response to performance feedback?
Bibliographic record
Abstract
Purpose When managers set aspirations for their firms, they typically compare their own firms' performance to past aspirations as well as to the performance of social reference groups. The authors explore how firm generic strategy affects managers' adaptation of firm aspirations in response to feedback from three social reference groups that vary in terms of breadth (population average, strategic group, and one direct rival). Design/methodology/approach The authors propose that firm generic strategy (low-cost or differentiation) functions as an organizational information filter through with managers interpret performance feedback. The authors test for whether generic strategy has a moderating effect on the influence of performance feedback from social reference groups. Findings Based on a longitudinal sample of US airlines, the study shows that all firms are influenced most strongly by their strategic groups. Low-cost and differentiation generic strategies differ in terms of which social reference group motivates a larger reaction when overperforming: low-cost firms are more influenced by the population average which is contributed to by the entire industry than are differentiating firms, while differentiating firms are more swayed by the narrow focus of their direct rivals than are low-cost firms. Originality/value Although firm strategy represents a core decision at the firm level, to the best of the authors’ knowledge, performance feedback research, surprisingly, has not yet integrated generic strategy into its models.
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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 | 0.016 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".