Bibliographic record
Abstract
Organizations must learn and adapt to succeed in today’s ever-changing business environment, so it is essential for scholars to better understand the antecedents to learning processes among individuals, teams, and organizations as a whole. In this article, the authors offer a multilevel theory that illustrates how individuals’ motivation for different achievement goals, that is, goal orientations, shape the way they individually and collectively participate in organizational learning processes. This framework is grounded in a theoretical synthesis of organizational learning and achievement goal theories, which highlights the value of using an emergent motivational theory to better understand how predominantly cognitive learning processes may emerge across levels in organizations. In particular, the authors illustrate how mastery- and performance-oriented norms emerge in work groups and influence information interpretation and integration. The authors further describe how groups’ goal orientation norms can become embedded in the organizational culture, which impacts the ways in which learning processes are institutionalized throughout the organization. This theoretical framework provides a fuller depiction of why and how learning unfolds in organizations, which may facilitate future research on the microfoundations of organizational learning and how these can enable organizations to enhance their capabilities.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".