Irish CIOsâ Influence on Technology Innovation and IT-Business Alignment
Bibliographic record
Abstract
Technology is the driving force behind many of todayâs new products, services, and cost-cutting measures. However, there are gaps in our understanding about how technological innovation is fostered and nurtured in organizations. Part of the answer is to examine how Chief Information Officers (CIOs) exercise influence regarding technological innovation in organizations. This is particularly important since the CIO is the head of technology in organizations, an important source of technological innovation. This article draws on an established executive influence framework to demonstrate how Irish CIOs are able to solidify Information Technologyâs (ITâs) contribution to technological innovation via relational means. Most of the CIOs in our study were able to successfully influence other executives to support these innovations which led to better IT-business alignment. However, other CIOs in our study were unsuccessful at influencing executives, which increased the disconnection between the CIO and the executive. Building on this study, we suggest significant practices and behaviors that CIOs can use to successfully influence other executives regarding technological innovations. CIOs must recognize that the relational side of technology alignment should be leveraged for them to successfully manage their contribution to technological innovation.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".