Open government data, innovation and diversification: the pursuit of economic value
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.
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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 | 0.015 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".