Analysis And Measurement Of The Impact Of Information Technology Investments On Performance In Mexican Companies: Development Of A Model To Manage The Processes, Projects And Information Technology Infrastructure And Its Impact On Profitability
Bibliographic record
Abstract
In Mexico, companies invest enormous resources in information technology (IT), with little evidence of the latters effectiveness. Company directors struggle with gauging how effective or ineffective making these investments truly is, given the lack of instruments of measurement by which to establish, for instance, an internal rate of return or a period of recovery on investments. There is also no evidence by which to link IT investment to improvements in a companys performance or profitability. While several American and Australian universities have developed studies that address these issues, for the most part these are limited to their respective countries and in some cases to Canada and Europe. Thus, there is a lack of empiric evidence in the Mexican scenario. Being able to analyze and measure the impact of IT investments is an important first step into making these resources more efficient. Based on the following analyses, one will identify the variables that intervene in successful and/or unsuccessful management of processes and projects, as well as in the administration of IT infrastructure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".