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Record W3003290254 · doi:10.5539/cis.v13n1p90

The Impact of Information Technology on Information System Effectiveness in Jordanian Telecommunication Companies

2020· article· en· W3003290254 on OpenAlexvenueno aff
Majd Mohammad Alhawamdeh, Shaker J. Alkshali

Bibliographic record

VenueComputer and Information Science · 2020
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceInformation systemManagement information systemsInformation technologySoftwareInformation securityScale (ratio)Sample (material)Information security managementTelecommunicationsKnowledge managementComputer securitySecurity information and event managementOperating systemCloud computing security

Abstract

fetched live from OpenAlex

This study aimed to test the impact of information technology on Information systems effectiveness in Jordanian Telecom Companies. The study adopted a five-dimensional scale to measure information technology (people, hardware, software, databases, and networks), while the information systems' effectiveness was measured through four dimensions: end-user satisfaction, system usage, system security and suitability of the system for management levels. To achieve study aims, a descriptive-analytical method was used. The study was conducted on a sample of (152) managers working in these companies. This study found that there is a high-level average for information technology dimensions and Information Systems effectiveness dimensions. Also, the results showed a significant impact of information technology dimensions (people, software, databases, and networks) except hardware on effectiveness information systems. It was also evident that there a significant impact of information technology on Information systems effectiveness dimensions (end-user satisfaction, system usage, system security and suitability of the system for management levels).

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.614
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.020
Open science0.0010.000
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 head0.009
GPT teacher head0.235
Teacher spread0.227 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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