Blooming Through the Cracks: The Effects of Fault Lines in Corporate Boards on Information-Technology 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
Corporate boards can significantly affect the value generated from information technology (IT) investments. An implicit assumption in the scarce research exploring this relationship is that boards operate as cohesive groups. Departing from the assumption, this study draws upon the literature on fault lines, which suggests that individual director attributes align or misalign, creating hypothetical dividing lines that split the board into multiple subgroups and influence their effectiveness. Based on extensive analyses of a panel of 2,463 firms over 2010-2020, we find that knowledge-based fault lines, which enhance knowledge diversity, enable IT value generation, and this effect is amplified in munificent environments, which motivate and enable the board subgroups to seek and share diverse knowledge. However, identity- and resource-based fault lines do not affect IT value generation. Thus, knowledge diversity on boards should be encouraged, but diversity based on social identities and resources might not matter when seeking IT value.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 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