Reciprocity between CIO Power and IT Innovation Leadership
Bibliographic record
Abstract
Senior IT managers’ ability to add value leads to a view of IT as a strategic priority rather than a mere support activity. When a firm adopts a strategic view of IT, one of the primary goals for that firm will be to achieve IT leadership in their industry. Numerous news articles and case studies documenting the practices of IT leaders, such as Wal-Mart, PNC Bank Corp. and Harrah’s Entertainment, highlight the more strategic responsibilities of their senior IT managers. The objective of this study is to examine the positive reciprocity between senior IT managers who act as a catalyst for the firm’s IT innovations and subsequent IT leadership, and the likelihood that IT leaders will consequently bestow more power on such managers.To examine the positive reciprocity between the role of senior IT manger and IT leadership, we obtained our data from Information Week 500 (IT leaders), Hoover’s Handbook of American Business (non-IT leaders), and other publicly-available sources, like Lexis-Nexis, and a variety of online sources (for senior IT manager’s name, official title, and number of titles) for the period 1997-2004. We find that as the senior IT manager’s power increases, the likelihood that the firm will achieve and maintain leadership in IT over their competitors also increases. Furthermore, our results show that as firms succeed in becoming the IT leaders in their industry, they will continue to reward their senior IT managers with more structural power.Our study has important strategic implications for a firm’s top management team and important career implications for senior IT managers. Top management teams that view IT as a strategic priority need to bestow their senior IT managers with increased structural power. Furthermore, senior IT managers are more likely to be value-adding contributors to the firms’ effort to be an IT leader if they know they will be rewarded with more structural power.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".