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Record W2002052444 · doi:10.1142/s0219649203000358

Achieving Organizational Flexibility and Competitive Advantage Through Information Systems Flexibility: A Path Analytic Study

2003· article· en· W2002052444 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

VenueJournal of Information & Knowledge Management · 2003
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsFlexibility (engineering)Competitive advantageRespondentKnowledge managementComputer scienceTable (database)Organizational performanceScale (ratio)MarketingBusinessData miningManagementEconomics

Abstract

fetched live from OpenAlex

This paper presents an empirical study to examine the relationship between IS flexibility, organizational flexibility, and competitive advantage. The study presumes IS usage and organizational learning as the intermediate variables. The study used a questionnaire survey to obtain responses from IS users. The survey was carried out with 296 user-respondents from 42 organizations across eight industrial sectors. For the purpose of gaining more insight into a variable, its dimensions were considered. These dimensions were evolved from the literature. The qualitative scales for the dimensions were explained with a scale table. The scale table was constructed using fuzzy possibility values. Each respondent used this table as a guideline before responding to each item in the questionnaire. The data analysis validates the relationship between IS flexibility, organizational flexibility, and competitive advantage. The results of path analysis confirmed that organizational flexibility and competitive advantage could be achieved through IS flexibility.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.006
Open science0.0000.000
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 head0.056
GPT teacher head0.359
Teacher spread0.304 · 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