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Record W2425098420

Exploring Self-Perceptions of Motivations in the Hospitality Industry

2016· article· en· W2425098420 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueScholarworks (University of Massachusetts Amherst) · 2016
Typearticle
Languageen
FieldPsychology
TopicHuman Behavior and Motivation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPerceptionHospitalityHospitality industryBusinessMarketingPsychologyTourismGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper focuses on increasing our understanding of employee motivation by applying two different but complimentary measures to unpack motivational issues in hospitality employees: the Ten Factor Model (Hersey & Blanchard, 1969; Kovach, 1987) and Alderfer’s ERG theory (Alderfer, 1972). As the third study in a longitudinal body of work, this study will surface data collected between 2000 and 2016 within the Canadian lodging industry. The value of this work is two-fold. First, it maintains the detailed characteristics of Ten Factor Model while associating it with an established needs-based motivational theory centred on basic human’s realms of existence, social, and growth needs. Second, it attempts to unpack contextual issues by exploring shifts in self-ranked motivational needs over time and, more specifically, over varied economic circumstances.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.080
GPT teacher head0.278
Teacher spread0.198 · 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