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Record W3151938393 · doi:10.1080/0960085x.2021.1890529

Understanding information systems success: a hybrid view

2021· article· en· W3151938393 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.

Bibliographic record

VenueEuropean Journal of Information Systems · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsStrategic information systemComputer scienceSoft systems methodologyInformation systemFraming (construction)Management scienceKnowledge managementProcess (computing)Management information systemsProcess managementOperations researchEngineering

Abstract

fetched live from OpenAlex

The information systems (IS) success model, introduced in 1992, provided IS research with a comprehensive set of dependent variables for project success. While the model addresses both process and variance considerations, the latter has dominated the research. Concurrently, the benefits of hybrid theories have been discussed in the literature, and there have been calls for an integrated view of IS success that includes the process perspective. We build on this momentum by presenting a hybrid model based on a longitudinal case study of the development and implementation of a patient-flow decision-support system at a large not-for-profit hospital. Our model remains true to the DeLone and McLean framing but elaborates on the process elements. The hybrid model expands our ability to analyse multiple dimensions of IS success and integrates diverse research findings into the IS success model, providing a revised version for future research to extend.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.008
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.169
GPT teacher head0.329
Teacher spread0.160 · 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