A snapshot of key information systems (IS) issues in Estonian organizations for the 2000s
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it