Bibliographic record
Abstract
<p>Data governance (DG) is a framework to manage data as a strategic enterprise asset. It is used to define and communicate an organization's accountability for data as well as decision rights, policies, standards, procedures, and compliance. As an emerging research topic, there is limited empirical research and theory development. Accordingly, this thesis builds upon Tallon, Ramirez, and Short's (2013) formative qualitative research that proposed the Theory of Information Governance (TIG). This thesis extends the TIG to develop the Refined Theory of Data/Information Governance, theorizes and tests hypotheses, and develops five lower-order constructs and two higher-order constructs. Exploratory factor analysis, confirmatory composite analysis, and structural equation modeling are used on survey data (N = 126, N = 227) from two separate groups of financial professionals. The findings quantitatively ascertain the composition of DG, offer a matrix of DG enablers, and identify DG as a source of competitive advantage and increased performance.</p>
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 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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.051 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".