A snapshot of key information systems (IS) issues in Estonian organizations for the 2000s
Bibliographic record
Abstract
Purpose The purpose of this paper is to highlight key information system (IS) issues in Estonian organizations for the mid‐2000s. This research is a follow‐up to an initial effort in the country in 1993, in which a similar theme was investigated. The primary objective of this present study was to compare and contrast the findings in the previous study with the present effort. Design/methodology/approach The Delphi method was used. Viewpoints of both information technology (IT) professionals and non‐IT professionals (business managers) in the country were sought across two rounds of the Delphi method. Findings The findings suggest the following: the past decade has produced salient changes in the ranking of key IS issues for Estonia; it appears that there is a convergence of opinions on key IS issues in both the Estonian public and private sectors; and there are significant differences in key IS issues across professional groupings (IT and non‐IT). Research limitations/implications The ranking of issues as opposed to rating issues was used in the data analysis. Ranking items is more challenging to participants and might be a limiting factor. The sample size of this study is small and perhaps a larger sample would yield better insights. Practical implications Those in charge of IT resources in Estonian organizations, as well as policy makers in the country, may benefit from the information provided herein. Such insights may facilitate better understanding of current key IS issues in the country. Originality/value This research offers a snapshot of key IS issues in Estonian organizations for the mid‐2000s. More importantly, this work complements a prior study on the same topic that was conducted in the country in the 1990s.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".