Board Interlocks with Information Technology Firms and Innovation Outcomes: A Resource Dependence Perspective
Bibliographic record
Abstract
Information technology (IT) innovation development within non-IT firms has been a key interest, but it is fraught with challenges because these firms lack sufficient IT knowledge. This study takes a resource dependence perspective to examine how engaging interlocking directorates with IT firms, or IT interlocks, affects non-IT firms’ innovation outcomes. Despite the acknowledged role of board interlocks in knowledge transfer, the role of IT interlocks in transferring IT knowledge has not been studied. Using a large-scale panel dataset of Chinese public firms between 2000 and 2020, our findings reveal that IT interlocks of non-IT firms positively impact their IT innovation by transferring IT knowledge, particularly when the interlocked IT firms are knowledge-intensive. Our research contributes to the information systems literature by affirming IT interlocks’ positive impact on innovation outcomes and highlighting the value of specific board relational capital in transferring external knowledge in need. It also offers practical implications for non-IT firms overcoming innovation challenges by establishing directorate connections with IT firms.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".