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Record W2162721931 · doi:10.1109/hicss.2001.927162

Organizational and technological infrastructures alignment

2005· article· en· W2162721931 on OpenAlexaff
Anne‐Marie Croteau, Shelby J. Solomon, Louis Raymond, François Bergeron

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsUniversité LavalUniversité du Québec à Trois-RivièresConcordia University
Fundersnot available
KeywordsAdaptabilityKnowledge managementFlexibility (engineering)Structural equation modelingComputer scienceEmpowermentInformation technology managementBusinessProcess managementEngineeringInformation systemManagementManagement information systems

Abstract

fetched live from OpenAlex

The objective of this empirical study is to find ways that best support business performance through the alignment of organizational and technological infrastructures. Adapting D. Tapscott and A. Caston's (1993) infrastructure themes, the organizational infrastructure includes components such as common vision, cooperation, empowerment, adaptability and learning, and the technological infrastructure addresses dimensions such as user involvement, connectivity, distributed computing, flexibility and technology awareness. Top managers from 104 organizations completed a questionnaire. The data analysis is performed using structural equation modeling techniques. The results indicate that the alignment of organizational and technological infrastructure enhances business 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.

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)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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 designTheoretical or conceptual
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

Citations63
Published2005
Admission routes1
Has abstractyes

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