MétaCan
Menu
Back to cohort
Record W2768085680 · doi:10.17722/ijme.v9i3.943

Do CRM dimensions improve Hotels occupancy rates? Evidence from the Moroccan Hospitality Sector

2017· article· en· W2768085680 on OpenAlexvenueno aff
Youssef Chetioui, Hassan Abbar, Zahra Benabbou

Bibliographic record

VenueInternational Journal of Management Excellence · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsOccupancyBusinessCustomer relationship managementHospitalityPerspective (graphical)Hospitality industryMarketingImplementationKnowledge managementComputer scienceTourismEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The noticeable lack of accepted and unified models of Customer Relationship Management generally leads to failures in the implementation of CRM projects, particularly when organizations adopt the limited technology perspective. In an attempt to evaluate the different perspectives of CRM, this research paper focus on the perspective that proposes that effective CRM implementations typically involve the four dimensions: (1) customer orientation (2) knowledge management (3) CRM organization, and (4) CRM technology. Our study evaluates the impact of CRM dimensions and hotel performance (occupancy rate) in Morocco. A sample of 80 Moroccan hotels was surveyed. Regression and other tests were used for analyses and interpretation. Our results reveal a significant positive impact of customer orientation, knowledge management, and CRM organization on occupancy rate. While CRM technology has been demonstrated to not significantly affect hotels’ occupancy rate.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.316
Teacher spread0.267 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Management ExcellenceSame topicCustomer Service Quality and LoyaltyFrench-language works237,207