IT and the Board of Directors: An Empirical Investigation into the “Governance Questions” Canadian Board Members Ask about IT
Bibliographic record
Abstract
ABSTRACT: In modern organizations, information technologies (IT) often help drive organizational strategies. As such, IT require both judicious planning and oversight. While executive oversight over IT is quite common nowadays, several studies indicate that due to the many benefits and risks associated with IT, more/better board-level oversight may be in order. Unfortunately, there is a scarcity of research on the involvement of board members in IT governance. We attempt to partially fill this gap by empirically examining the degree to which the 27 IT governance questions that make up an IT board governance framework recommended by the Canadian Institute of Chartered Accountants are raised by the board members of 94 Canadian firms. We also investigate the extent to which the questions are considered important. Our findings show that: board members use only some of the IT governance questions and not all the recommended ones; there is a gap between the IT governance questions board members ask and the ones they perceive to be important; and the number and importance of IT governance questions that board members ask appear to vary with both their organization’s strategic use of IT and the need for IT reliability. Implications for research and practice are offered.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".