Open government data, innovation and diversification: the pursuit of economic value
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose There is a widely held belief that open government data (OGD) have the potential to generate both economic and social value. This study aims to empirically unpack the relationship between OGD, diversification activities and innovation in the pursuit of economic value creation by firms. Design/methodology/approach Using a matched sample comparison method and difference-in-differences analyses, the authors study the impact of OGD on innovation over time in the USA. The authors considered the open government directive in the end of 2009 in the USA as a policy intervention and collected 10 years of financial data of 79 firms that use OGD and 79 matched control firms in the USA. The authors compare US firms using OGD, with matched control firms, regarding the firms’ level of product diversification as a measure of innovative use of OGD. Findings The authors provide empirical evidence that OGD policy contributes toward innovation, and hence economic value creation, through product diversification. Firms that leverage OGD show superior product diversification in comparison to the matching control firms. The results suggest that OGD contribute to firms’ innovation and pursuit of economic value, as evidenced by their increased product diversification. Originality/value Although the extant literature concerning OGD has underscored the impact of OGD on innovation and economic value generation, there is a lack of empirical evidence in the literature. This study seeks to add to the extant literature by providing empirical evidence that contributes to the understanding of the relationship between OGD, diversification and innovation in the pursuit of economic value creation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it