Understanding the Transformation of the IT Function in Organizations.
Bibliographic record
Abstract
Many IT researchers have tried to describe the IT function and to explain its transformation over time. Nevertheless, we observed that existing characterizations are often based on a single dimension, attached to historical periods or built into a normative discourse that calls for an ideal profile. We do not subscribe to these premises, seeing that there might be a series of distinct archetypes for the IT function, and that each archetype may adapt and evolve in response to organizational and environmental parameters. Based on a literature review, we propose a typology of the roles of IT functions, within archetypes that are defined according to four dimensions: the IT function’s main activities, the skills of IT professionals, the interface between the IT function and the organization’s business units, and the IT function’s governance. Next, using the theory of punctuated equilibria as a foundation, we will apply the proposed typology to investigate the process by which IT functions evolve over time. From a methodological standpoint, we will first conduct a series of interviews with IT executives to validate the proposed typology. Second, we will conduct a longitudinal case study in the healthcare sector to explain how and why an IT function transforms over time and discover forces that foster stasis or inspire change. Ultimately, our study will provide a new conceptual and theoretical perspective on the role and transformation of IT functions in organizations.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".