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Record W2899431757 · doi:10.5539/ibr.v11n12p1

The Impact of Information Technology Management, Training and Strategy Management on Organizational Performance of Sharjah Police

2018· article· en· W2899431757 on OpenAlexvenueno aff
Abdulla Awadh Abdulla Abdulhabib, Hassan Saleh Al-Dhaafri

Bibliographic record

VenueInternational Business Research · 2018
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge managementOrder (exchange)Organizational performanceAffect (linguistics)Information technologyBusinessPsychologyComputer science

Abstract

fetched live from OpenAlex

The study mainly aims at investigating the impact that happens to the organizational performance as an impact of utilizing both information technology management and training. In-depth investigation of literature indicates the necessity of the proposal of the current research. The current study has utilized various theories including Knowledge Based View (KBV) and Resource Based View of the firm (RBV) so as to achieve the main purpose of the research. It also aimed at examining the impact of IT management and training on the performance of the organization. The study used a questionnaire as a tool. The researcher distributed a number of 341 questionnaires randomly on some department of the police in Sharjah. When the questionnaires were filled, the researcher used SPSS system in order to accurately analyze the results. The study concluded that the organizational performance of the Sharjah police has been positively affected by using information technology management. In addition, the research shows that it is essential to utilize on information technology management, training and strategy management on order to affect the performance of the organization successfully.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.341
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

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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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