MétaCan
Menu
Back to cohort
Record W2611273344 · doi:10.4018/irmj.2017070101

Information Systems Quality and Success in Canadian Software Development Firms

2017· article· en· W2611273344 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInformation Resources Management Journal · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsCarleton University
Fundersnot available
KeywordsQuality (philosophy)Information systemSoftware qualityBusinessInformation qualityProcess managementSoftware developmentProcess (computing)SoftwareCapability Maturity ModelInformation technologyKnowledge managementMarketingComputer scienceEngineering

Abstract

fetched live from OpenAlex

For years, firms have been investing millions of dollars in information systems (IS) to gain operational and strategic benefits. However, in most cases these expected benefits have not been realized because the software development community has been plagued with the delivery of low quality and unsuccessful information systems. Duggan and Reichgelt's information systems quality model was adapted with minor modifications to explore the impact of process maturity and people on IS quality in Canadian software development firms. The study also investigated the impact of IS quality on IS success. Using PLS-Graph as the statistical tool, it was discovered that people skills and contribution had the greatest impact on IS quality and that IS quality impacted IS success. These findings are important to both IS practitioners and researchers in their desire to deliver high quality and successful information systems in Canada.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.572
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0070.018
Open science0.0010.000
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 head0.046
GPT teacher head0.285
Teacher spread0.239 · 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