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Record W4311783409 · doi:10.5267/j.ijdns.2022.9.009

The influence of the Internet of things on pharmaceutical inventory management

2022· article· en· W4311783409 on OpenAlexvenueno aff
Dojanah Mohammad Kadri Bader, Ibrahim Atoum, Farah faisal Alathamneh

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetBusinessSample (material)Quality (philosophy)Structural equation modelingTest (biology)Cloud computingPopulationKnowledge managementMarketingControl (management)Computer scienceWorld Wide WebSociology

Abstract

fetched live from OpenAlex

The primary goal of this research is to examine the influence of the Internet of Things on Pharmaceutical Inventory Management in Jordan. The employees of the pharmaceutical companies in Jordan represent the population of the study. Accordingly, the study instrument was distributed to an appropriate sample of 620 employees of pharmaceutical companies in Jordan; the questionnaires used in the analysis were 432. Structural equation modeling (SEM) was used to test the study hypotheses. The study results indicated that all dimensions of the Internet of Things had an influence on inventory management. The greatest influence was for cloud computing. Based to the study findings, pharmaceutical companies in Jordan should provide high-quality Internet coverage in their offices and stores, as well as to invest in Internet of things technologies that allow the company to remotely monitor and control the physical components of the stores, attract employees with knowledge of modern Internet technologies, and provide the necessary training and development for their employees.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.296
Teacher spread0.271 · 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 designObservational
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

Citations12
Published2022
Admission routes1
Has abstractyes

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