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Record W4389775582 · doi:10.17705/1atrr.00083

Richness of IT Use Operationalization: A Conceptual Replication

2023· article· en· W4389775582 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAIS Transactions on Replication Research · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversité du Québec à MontréalRoyal Military College of CanadaHEC Montréal
FundersHEC Montréal
KeywordsOperationalizationReplication (statistics)Species richnessStructural equation modelingAmazon rainforestKey (lock)PsychologyComputer scienceData scienceBiologyEpistemologyEcologyStatisticsMathematicsMachine learningPhilosophy

Abstract

fetched live from OpenAlex

Use of information technology (IT) remains a key concern for organizations. This article presents a conceptual replication of Burton-Jones and Straub’s (2006) study, exploring the effect of IT Use operationalization richness – lean and rich – on Performance. We used 352 valid responses from Amazon MTurk through an online survey. Consistent with the original study, the hypothesis was tested by using the Structural Equation Modeling technique. Our results – which indicated support for the same hypothesis in the original study – suggest that the richer the IT use operationalization, the higher the individual Performance.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.517
GPT teacher head0.536
Teacher spread0.020 · 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