Distributed IT championing: A process theory
Bibliographic record
Abstract
Championing is key to the success of an IT implementation. Recently, changes in the nature of technologies used in organizational contexts and changing organizational structures call for a renewed focus on IT championing to explain its distributed nature. Following an analytic induction approach and drawing from semi-structured interviews with 37 practitioners (physicians, residents, nurses, IT staff, and administrators) in three healthcare-related settings, the study conceptualizes distributed IT championing as a process constituted of multiple individuals’ behaviors, unfolding over time, that proactively go beyond formal job requirements in support of an IT implementation. While multiple individuals may enact similar championing behaviors, the data indicate that multiple individuals may also enact distinct, yet complementary, championing behaviors over the course of the IT implementation. Overall, distributed IT championing evolves through cycles of distinct stages of bridging-in, bonding, and bridging-out, with each stage being shaped by different dimensions of social capital. Also, IT artifacts that are particularly generative appear more conducive to distributed IT championing than closed ones. This article contributes to extant literature on IT championing by developing a process model of distributed IT championing in the context of an IT implementation.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".