CIO Lateral Influence Behaviors: Gaining Peers’ Commitment to Strategic Information Systems1
Bibliographic record
Abstract
In order to develop and bring to fruition strategic information systems (SIS) projects, chief information officers (CIOs) must be able to effectively influence their peers. This research examines the relationship between CIO influence behaviors and the successfulness of influence outcomes, utilizing a revised model initially developed by Yukl (1994). Focused interviews were first conducted with CIOs and their peers to gain insights into the phenomenon. A survey instrument was then developed and distributed to a sample of CIO and peer executive pairs to gather data with which to test a research model. A total of 69 pairs of surveys were eventually used for data analysis. The research model was found to be generally meaningful in the CIO–top management context. Furthermore, the influence behaviors rational persuasion and personal appeal exhibited significant relationships with peer commitment, whereas exchange and pressure were significantly related to peer resistance. These results provide useful guidance to CIOs who wish to propose strategic information systems to peers.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".