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Record W2752877305 · doi:10.5937/ekopre1704294s

A survey of the use and purpose of spreadsheetsin SMEs in Serbia

2017· article· en· W2752877305 on OpenAlexaboutno aff
Marton Sakal, Lazar Raković, Vuk Vuković

Bibliographic record

VenueEkonomika preduzeca · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)Quarter (Canadian coin)Task (project management)Work (physics)SerbianComputer scienceMarketingBusinessManagementEngineeringStatisticsMathematicsGeography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.177
GPT teacher head0.298
Teacher spread0.121 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

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