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Record W2744094435 · doi:10.5539/mas.v11n9p1

Employee Performance and Quality Management in the Tourism Sector (Case Study of Human Resources Management – Employee Performance)

2017· article· en· W2744094435 on OpenAlexvenueno aff
Burhan M Awad Al-Omari, AlaEldin Mohammad Hasan Awawdeh, Main Naser Alolayyan

Bibliographic record

VenueModern Applied Science · 2017
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsTourismBusinessMarketingQuality (philosophy)Hospitality management studiesService (business)Service qualityCompetition (biology)Hospitality industryVariety (cybernetics)HospitalityHuman resourcesCompetitive advantageQuality managementManagementEconomicsComputer scienceGeography

Abstract

fetched live from OpenAlex

The subject of quality is at the forefront of strategic plans for any business organization and institution to offer tourist services in the field of hotels. This becomes one of the priorities due to competition in reaching to the largest possible segment of customers. This is the need of time for the organization to master in quality, competencies and expertise in a variety of fields. The problem is how to manage quality for outstanding application in the service sector, tourism and hospitality by improving process and customer satisfaction.This paper focuses on five stars hotel of the city of Aqaba, Jordan. The importance of this approach in general and tourism organization particular is very high. This study presents the descriptive analysis, limitation and treatment of change that represented by service diminution's. The importance of total quality management in the tourism sector is well understood. The study presents the managerial art that is applied in one of five star hotels as a 1st degree of associate mixed company (Aqaba Hotel – Jordan). And I hope that the research modestly contribute to the performance of hotel organizations to meet the competitive challenges.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0040.002
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 head0.046
GPT teacher head0.290
Teacher spread0.244 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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