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Record W7072312557

Work motivation, job satisfaction and turnover intention of middle level management at five star hotels in Kuala Lumpur / Nur Dalila Mohd Fisol

2013· other· en· W7072312557 on OpenAlexaboutno aff

Bibliographic record

VenueUiTM Institutional Repositories (Universiti Teknologi MARA) · 2013
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicHospitality and Tourism Education
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryHospitality industryWork (physics)Kuala lumpurQuarter (Canadian coin)Economic shortageJob satisfactionTourismHotel industry
DOInot available

Abstract

fetched live from OpenAlex

The hospitality and tourism is one of the largest economic contributors for Malaysia (Economic Impact, 2012). Malaysia is emerging as a fast growing free market economy with services as the main source of economic growth accounting for almost three quarter of Malaysia’s output with 26 percent of its labor force (MIS Asia, 2010). In general, by 2020, there will be a total of 1.6 billion jobs in the hotel industry (Travel & Tourism, 2011). Thus, there is a significant need in having a more qualified, motivated and skilled manpower available to meet the challenges of this ever changing and demanding industry (Ahmad, Aziz, Kamaruddin, Aziz & Bakhtiar, 2012). Similar to the developed countries, hotels in Malaysia are facing problems in attracting and retaining skilled and knowledgeable of high and low level employees. This is due to low salary and rigid job traits (Ahmad, Solnet & Scott, 2010). The rapid expansion of the hotel industry has exaggerated the demand for employment of competent employees (Ahmad & Zainol, 2011), and this resulted in skilled and knowledgeable workers shortages (Business Monitor International Ltd., 2010). On the other hand, the shortage of skilled workers in Malaysian hotels is caused by the unattractive work atmosphere and unmotivated factors of the industry (Kasimu, Zaiton & Hassan, 2012).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.202
Teacher spread0.179 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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