IT investment management and information technology portfolio management (ITPM)
Bibliographic record
Abstract
Purpose – The purpose of this paper is to analyze some Brazilian companies’ use of the information technology portfolio management (ITPM) technique as an aid to their information technology (IT) investments management. Design/methodology/approach – It was carried out in five case studies in different Brazilian companies from several economic sectors which were using ITPM or were in the initial implementation phase. Eight interviews were conducted. The persons interviewed were high-level executives working in the IT department in the studied companies. Findings – Different levels of ITPM use was found with respect to IT investment management (planning, control and evaluation). It was observed, in the analyzed cases, that ITPM is used most frequently in IT investment planning, which is the process most discussed and used in analyzed companies. The ITPM technique is used more frequently in Company 2 than in the other cases because the organization of the IT area in the company is structured according to ITPM dimensions. Research limitations/implications – The ITPM technique has received little attention in IT research and research in this area identifying the use and applicability of ITPM in companies is still very limited in the information systems literature. Originality/value – The paper presents IT investment management in different Brazilian companies and how ITPM was used to help companies in this process compose by planning, control and evaluation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".