The Impact of Governmental Policies in Institutional Fields: The Case of Innovation in the Dutch Concrete Industry
Bibliographic record
Abstract
The creation of markets for new products involves interplay between various field constituents. A major challenge in this process is to establish a sufficient level of legitimacy in order for a market to become accepted in the organization field. Yet, this process of market creation may be suppressed by established institutional arrangements that actively block the diffusion of innovations and constrain change. In this paper, we examine the roles of regulatory structures, professional associations and competitors in market suppression. We pay particular attention to the actions and circumstances preventing change in a mature sector of the economy, despite state policies directed at change. Active resistance from professional associations and corporate actors inhibited creation of a new market. We draw upon institutional theory to provide an account of the different communities of interest involved in an institutional field, exploring how they are developed and negotiated. We argue that the role of the government and the impact of its policies in market construction may be overemphasized and that the complexity of its interaction with organizational fields may be underestimated.
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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".