Organizational Structure from Interaction: Evidence from Corporate Sustainability Efforts
Bibliographic record
Abstract
We advance interactionist perspectives on how organizational structures emerge in new issue domains. Our study is grounded in field data collected over 18 months at a large biomedical company that sought to become more sustainable. Over that period, some sustainability-related issues became firmly embedded in formal structures and procedures, while others faltered. We identify the quality of situational interactions among organizational members as the engine behind the structuring of organizational sustainability efforts. Successful interactions generated traces of attention, motivation, knowledge, relationships, and resources that linked fleeting interactions to emergent organizational structures. Our findings point to the importance of internal advocates and distributed processes at middle and lower levels for developing organizational structures, and we show that advocates’ interests, commitments, and identities are altered in the course of repeated interactions, as are the political resources available to them. Paying attention to situation-level interactions thus results in a more dynamic view of the emergence of formal structures through political processes. We develop a process model that informs structuration perspectives on organizational change by showing how social interaction dynamics can account for divergent levels of structuring within the same domain. The model also advances political perspectives on organizational change by unpacking the situational underpinnings of advocacy efforts and collective mobilization around issues.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".