Institutional Entrepreneurship as Embedded Agency: An Introduction to the Special Issue
Bibliographic record
Abstract
We are delighted to introduce this special issue of Organization Studies ,t he purpose of which is to develop a deeper understanding of the concept of institutional entrepreneurship and to offer new avenues for future research. This concept has been attracting considerable attention in recent years, as was reflected in the record number of papers that were submitted ‐ the largest number that this journal has received for any of its special issues to date. As a result, the selection process has been stringent and we are very pleased to present the eight articles in this special issue, all of which survived the demanding review process. Each of these articles contributes important insights to our understanding of institutional entrepreneurship and, collectively, they provide an important benchmark for subsequent research on this phenomenon. In different ways, they explore how actors shape emerging institutions and transform existing ones despite the complexities and path dependences that are involved. In doing so, they shed considerable light on how institutional entrepreneurship processes shape ‐ or fail to shape ‐ the world in which we live and work The term institutional entrepreneurship refers to the ‘activities of actors who have an interest in particular institutional arrangements and who leverage resources to create new institutions or to transform existing ones’ (Maguire, Hardy and Lawrence, 2004: 657). The term is most closely associated with DiMaggio (1988: 14), who argued that ‘new institutions arise when organized actors with sufficient resources see in them an opportunity to realize interests that they value highly’. These actors ‐ institutional entrepreneurs ‐ ‘create a whole new system of meaning that ties the functioning of disparate sets of institutions together’ (Garud, Jain and Kumaraswamy, 2002). Institutional entrepreneurship is therefore a concept that reintroduces agency, interests and power into institutional analyses of organizations. It thus offers promise to researchers seeking to bridge what have come to be called the ‘old’ and ‘new’ institutionalisms in organizational analysis (Powell and DiMaggio, 1991; Greenwood and Hinings, 1996). We preface these papers with some of our own observations on institutional entrepreneurship stemming from its paradoxical nature. Research on institutions has tended to emphasize how organizational processes are shaped by institutional forces that reinforce continuity and reward conformity. In contrast, the literature on entrepreneurship tends to emphasize how organizational processes
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".