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Record W4394885784 · doi:10.5267/j.uscm.2024.2.003

The effect of knowledge management on organizational performance in the service sector: The role of transformational leadership as the moderating

2024· article· en· W4394885784 on OpenAlexvenueno aff
Hamzeh Alhawamdeh, Haitham Ali Hijazi, Baha Aldeen Mohammad Fraihat, Abbas Mohammad Alhawamdeh, Fadi Shehab Shiyyab, Manaf Al‐Okaily

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsTransformational leadershipNonprobability samplingBusinessTourismAffect (linguistics)PopulationBusiness administrationService (business)ModerationOrganizational cultureKnowledge managementTertiary sector of the economyMarketingPsychologyManagementSocial psychologyEconomicsComputer sciencePolitical scienceSociology

Abstract

fetched live from OpenAlex

The main aim of the current study is to assess the relationship between knowledge management (KM) capabilities and organizational performance (OP) in developed nations. In addition, this study also aims to assess the moderating influence of transformational leadership (TL) between KM infrastructure (KMCI) and organizational performance (OP) of the Jordanian service and tourism sectors. In this study, it’s suggested that KMCI and its components will directly affect OP. In addition, it’s expected that TL moderates the relation between study variables. Purposive sampling was used to gather data from the service and tourism sectors in Jordan, as it is the population of this study. Partial Least Squares was used to analyze the data. The findings indicate that KMCI and its components significantly affect OP. TL moderated the effect of KMCI on OP. It is advised for decision-makers to concentrate on KMCI, create the proper culture and organizational structure, and improve its technologies to support the growth of KM activities in Jordanian service and tourism sectors.

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)0.002
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 score0.867
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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