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Record W2142073111 · doi:10.5430/ijba.v4n1p39

Strategic Human Resource Development in Hospitality Crisis Management: A Conceptual Framework for Food and Beverage Departments

2013· article· en· W2142073111 on OpenAlexvenueno aff
Ahmad Puad Mat Som, Albattat Ahmad

Bibliographic record

VenueInternational Journal of Business Administration · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
FundersUniversiti Sains Malaysia
KeywordsHuman resource managementBusinessContext (archaeology)Crisis managementHospitality industryHospitalityConceptual frameworkHuman resourcesStrategic human resource planningStrategic managementProcess managementKnowledge managementResource management (computing)Organizational behavior and human resourcesMarketingStrategic planningOrganizational performanceManagementPolitical scienceEconomicsSociologyComputer scienceTourism

Abstract

fetched live from OpenAlex

Crisis management has been a largely unnoticed territory in human resource development. Despite the increased impact of organizational crises on individual and organizational performance, it remains to be an issue that must be recognized and addressed. This paper reviews the current literature on hotel industry crisis management, its progression and effective crisis management framework. Garavan`s strategic human resource model as a guiding framework is discussed to help understand the various ways in which human resource development can build crisis management capabilities in organizations. The study applies various components of the model to the crisis management context and integrates ideas from the literatures. The paper offers specific guidelines for practitioners regarding how to align strategic human resource development with food and beverage department strategies and identify areas for future research.

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)0.005
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.003
Science and technology studies0.0040.018
Scholarly communication0.0100.009
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.291
Teacher spread0.251 · 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 designTheoretical or conceptual
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

Citations12
Published2013
Admission routes1
Has abstractyes

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