Individuals’ IT-Change Readiness in Healthcare Organizations
Bibliographic record
Abstract
IT-change readiness is an essential element of success in organizational information technology (IT) change intervention success for healthcare organizations. Change readiness research typically disregards the needs of individual constituents. We describe the experiences of medical professionals, IT professionals and managers in a medical organization in preparing for major IT-change due to a merger. We conducted a qualitative study in two medical organizations in the Netherlands via 18 in-depth semi-structured interviews using a replication logic. We analyzed the data using selective coding and thematic coding for grounded theory development. Six themes emerged from our study as factors contributing to individuals’ IT-change readiness: These are (1) individuals’ IT use frequency, (2) IT self-efficacy, (3) IT enjoyment, (4) anticipated IT usefulness, and (6) commitment. IT self-efficacy was influenced positively by people’s education level and inversely by their age. We identified sub-themes for given themes and developed propositions for future testing and generalization of our findings. These factors may be used in practice for hiring, promotion and training decisions.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".