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Record W2913475823 · doi:10.6000/1929-7092.2019.08.11

4.0 Leadership Skills in Hospitality Sector

2019· article· en· W2913475823 on OpenAlexvenueno aff
Maria José Sousa, Vasco Santos, António Sacavém, Isabel Reis, Marta Correia Sampaio

Bibliographic record

VenueJournal of Reviews on Global Economics · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHospitality and Tourism Education
Canadian institutionsnot available
Fundersnot available
KeywordsHospitalityHospitality industryPerspective (graphical)Public relationsLeadership studiesTransformational leadershipSociologyPsychologyMarketingManagementKnowledge managementLeadership styleBusinessPolitical scienceTourismComputer scienceEconomics

Abstract

fetched live from OpenAlex

This paper intends to analyses leadership skills in the hospitality sector in the era of 4.0 industry. The purpose is to explore the role of multi-level forms of leadership and the profiles identified by the hospitality professionals. This is a quantitative study based on an online survey applied to two hotels, and the following research question have guided the present study: What are the 4.0 Leadership Skills in the hospitality sector? To answer the research question, the main technique to collect data was a questionnaire allowing to investigate the main issues related to 4.0 leadership skills. The results of the research are the identification of the leadership skills profiles, being this research significant for managers and leaders when developing organizational interactions from a multi-level efficacy perspective. The conceptual contribution of the paper is a fresh macro-analytical perspective concerning 4.0 leadership skills in the hospitality sector.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.037
GPT teacher head0.250
Teacher spread0.213 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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