Chasing two hares at once: The effects of goal orientation (in)congruence in teams
Bibliographic record
Abstract
Abstract Organizations must excel at what they do well while also learning new ways of operating to achieve long‐term success. Work teams may thus find themselves pursuing contradictory objectives to support the organization's strategy. We investigated teams' goal orientation (in)congruence and its impact on task meaningfulness and, ultimately, performance, hypothesizing the potential pitfalls of teams simultaneously pursuing both learning‐ and performance‐goal orientations. Three‐wave, multisource data were collected from 109 teams at a large North American mortgage company. In a polynomial regression and response surface analytical framework, team task meaningfulness—and subsequent team performance—was enhanced when teams had greater divergence between their learning‐ and performance‐goal orientations but suffered when both goal orientations were more aligned. Our investigation thus revealed the potential pitfalls of teams simultaneously pursuing both learning‐ and performance‐goal orientations. We discuss the theoretical contributions of the team goal orientation incongruence effect substantiated in this study, as well as implications for practice and future research.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".