A survey of the use and purpose of spreadsheetsin SMEs in Serbia
Bibliographic record
Abstract
Due to their unique simplicity and flexibility, spreadsheets are nowadays used for various purposes, from financial calculations, planning and data aggregation, to decision making at different levels of management. Despite being created with the intention of being of temporary character, research shows that spreadsheets tend to provide support even in key business processes in organizations, often over longer periods of time. Starting from the related work mentioned in the paper, and prompted by issues to which articles dealing with spreadsheet errors especially drew attention, the objective of this research was to answer the following questions: Are spreadsheets used in SMEs and to what extent? How great is the significance of spreadsheets in respondents' regular activities? In which situations and for what purpose do respondents use spreadsheets in SMEs? The research encompassed 213 respondents from 147 Serbian SMEs. Among other things, research results have shown that more than 90% of respondents use spreadsheets to a certain extent, most frequently MS Excel. Almost three quarters of respondents regard spreadsheets as important for performing their work. More than two thirds of respondents have more than four years of spreadsheet experience, using them most frequently up to one quarter of their working hours, usually as an auxiliary tool, as follows: more than a quarter of respondents use spreadsheets when they cannot perform the task with the existing IS, and as many as 60% when they find it easier to perform their task with spreadsheets than using the existing official IS.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".