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Impact of RFID Technology on Health Care Organizations

2010· book-chapter· en· W4239047313 on OpenAlexaff
Véronique Nabelsi, Florina Stefanescu

Bibliographic record

VenueIGI Global eBooks · 2010
Typebook-chapter
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsRadio-frequency identificationBusinessSupply chainContext (archaeology)Variety (cybernetics)Government (linguistics)Identification (biology)Investment (military)MarketingIndustrial organizationComputer scienceComputer security

Abstract

fetched live from OpenAlex

RFID technologies represent a common standard for data storage and retrieval that could improve collaboration and data sharing between noncompeting organizations. With the advent of RFID (radio frequency identification), organizations have the opportunity to rethink how their organization will be. Unlike companies in the United States and Europe which are mandated by large retailers or government departments, most Taiwan companies are investing in RFID without pressure. The article explores the factor affecting radio frequency identification adoption applications in Taiwan. Its objective is to summarize the ways in which organizations are thinking about their possible uses in a wide variety of companies and industries. An empirical investigation (n=134) found seven factors affecting RFID adoption within Taiwan. They are operation efficiency, manufacturing efficiency and supply chain efficiency, organization context, investment cost, market environment, and technology characteristic. By providing insight into these important factors, this article can help further understanding of their role in the adoption and use of RFID. The theoretical and practical implications of these results are discussed.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.005
GPT teacher head0.254
Teacher spread0.249 · 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 teacher head, not a consensus.

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

Citations1
Published2010
Admission routes1
Has abstractyes

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