Bibliographic record
Abstract
Research on the strategic management of Information Technology (IT) resources has mostly focused on the oversight provided by the management team as a means to increase organizational performance. In recent years, boards of directors have also increased their involvement in IT matters, and various theoretical lenses suggest that this oversight too has the potential to influence organizational performance. Hence, this study synthesizes the resource-based and contingency views of MIS with corporate governance theories, and examines key antecedents and consequences of board-level IT governance (ITG) using a multi-method approach. Structural Equation Modelling analysis applied to organization-level data collected from 171 board members suggested that the level of ITG exercised by boards was contingent upon the organization's ‘IT use mode’, along the two dimensions of need for (a) fast and reliable IT, and (b) new innovative IT. But, the findings further suggested that the contingency approach may be suboptimal because it can cause new ways of leveraging IT to be ignored. High levels of board-level ITG, regardless of existing IT needs, increased organizational performance. This phenomenon was illuminated with applicability checks. Moreover, content analysis and structured interviews with board members further enriched these insights.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".