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Record W2087938167 · doi:10.4018/jgim.2005070104

Information Systems Effectiveness in Small Businesses

2005· article· en· W2087938167 on OpenAlexaffabout
Ana Ortíz de Guinea, Helen Kelley, M. Gordon Hunter

Bibliographic record

VenueJournal of Global Information Management · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversity of LethbridgeQueen's University
Fundersnot available
KeywordsVendorBusinessSmall businessContext (archaeology)Sample (material)Construct (python library)MarketingQuality (philosophy)Knowledge managementInformation qualityInformation systemProcess managementComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This study examines the applicability of the Thong, Yap, and Raman (1996) model of information systems (IS) effectiveness tested among Singaporean small businesses in a Canadian context. The model evaluates the importance of managerial support and external expertise (vendors and consultants) for IS effectiveness. This study extends the Thong et al. model by adding an intention of expansion construct. The sample included 105 small business users of IS in a small city in western Canada. The results show that both managerial and vendor support are essential for effective IS in Canadian small businesses, and supported part of the relations between IS effectiveness and intention of expansion. Overall, the results suggest that managers should engage quality vendors to obtain IS that contribute to the specific goals of the small business. The results of the Canadian study were, for the most part, similar to the results reported in the Singaporean study; however, a few notable differences appear to exist.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.084
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.340
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), 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

Citations68
Published2005
Admission routes2
Has abstractyes

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