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Record W4391060580 · doi:10.5267/j.uscm.2023.11.011

The mediating role of ICT on the impact of supply chain management (SCM) on organizational performance (OP): A field study in Pharmaceutical Companies in Jordan

2024· article· en· W4391060580 on OpenAlexvenueno aff
Hazem Khaled Shehadeh, Ahmad A. I. Shajrawi, Munif Zoubi, Mohammad Khalaf Daoud

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
FundersZarqa University
KeywordsBusinessInformation and Communications TechnologySupply chainStock exchangeSupply chain managementKnowledge managementMarketingDescriptive researchPopulationSample (material)Computer scienceMedicine

Abstract

fetched live from OpenAlex

This research aims to identify the Mediating role of ICT on the Impact of supply chain management (SCM) on organizational performance (OP), a field study: of pharmaceutical companies in Jordan. To achieve the aim of the research, the researcher used the descriptive analytical approach. The research population is all the employees in the three pharmaceutical companies listed on the Amman Stock Exchange (1,528), A suitable sample content of (400) employees was chosen, questionnaires were distributed using Google Forms, and the percentage of correct questionnaires was (85%), The research concluded that SCM with its dimensions has an impact on OP in pharmaceutical companies in Jordan, CRM does not exhibit a notable impact on the dependent variable OP and this research provides robust support for ICT mediating the relationship between SCM and OP in pharmaceutical companies in Jordan. The research recommended pharmaceutical companies to explore strategies to enhance their customer relationships and it also recommends pharmaceutical companies to invest in and enhance ICT infrastructure and capabilities, this research also recommends future studies to examine the role of artificial intelligence (AI) instead of (ICT) mediator between (SCM) and (OP).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.016
GPT teacher head0.278
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations18
Published2024
Admission routes1
Has abstractyes

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