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Record W2925374900 · doi:10.5539/mas.v13n4p137

The Relationship between Organizational Culture and Organizational Commitment

2019· article· en· W2925374900 on OpenAlexvenueno aff
Dima H. Aranki, Taghrid Suifan, Rateb J. Sweis

Bibliographic record

VenueModern Applied Science · 2019
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsOrganizational commitmentOrganizational cultureSample (material)BusinessOrganizational learningRegression analysisOrganization developmentPsychologyJob satisfactionOrganizational behavior and human resourcesBusiness administrationKnowledge managementPublic relationsSocial psychologyPolitical scienceComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

The main purpose of this research is to investigate the relationship between organizational culture and organizational commitment, in Information Technology (IT) companies in Jordan. A convenience sample was selected from employees working in 24 IT companies in Jordan. A total of 711 questionnaires were distributed among them; 371 were returned, and 342 were valid for statistical analysis, resulting in response rate of 52 percent. Linear regression analysis was also used to test the hypothesis. The results of the analysis indicated that there is a positive and significant relationship between organizational culture and organizational commitment. Based on the results, the research provides several recommendations. IT companies in Jordan should place emphasis on building better culture, in order to achieve higher levels of organizational commitment. The research also suggests that future research should take job satisfaction as a mediator variable of the relationship between organizational culture and organizational commitment.

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)0.009
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
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)0.0030.000

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 head0.017
GPT teacher head0.226
Teacher spread0.210 · 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

Citations104
Published2019
Admission routes1
Has abstractyes

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