AVALIAÇÃO DE SISTEMAS DE INFORMAÇÕES: UMA PESQUISA SOBRE A SATISFAÇÃO DE USUÁRIOS NA REGIÃO METROPOLITANA DE BELO HORIZONTE MINAS GERAIS
Bibliographic record
Abstract
This paper presents the results of a survey that aimed to analyze the different groups of information systems (ISs) users in organizations and their different perceptions about them. This research can be classified as descriptive and a quantitative approach. The used sample was a non-probabilistic one with a total of 335 interviewed users. Primary data was collected through a questionnaire that assessed their satisfaction with the ISs and the information provided by such systems. Data analysis was performed using the following techniques: (i) Descriptive statistics (with a focus on cross-tabulations); (ii) Chi-square test; (iii) Kruskal-Wallis test; (iv) Analysis of variance (ANOVA); and (v) Cluster analysis. In general, ISs’ users were satisfied regarding to ISs used in their companies, as well as the information provided by those systems. However, two aspects showed greater dissatisfaction among the respondents: the ISs’ Flexibility and the necessity of (re) typing data. It was used cluster analysis aiming to group ISs’ users according to their perceptions about the systems. Based on this analysis, three clusters were estimated, identified and labeled as follows: Cluster 1 – “Satisfied with the IS”; Cluster 2 – “Satisfied with the information”; and Cluster 3 – “Widely Satisfied”.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".