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Record W4328025162 · doi:10.5267/j.uscm.2023.1.014

The effect of social customer relationship management on customer loyalty in Saudi Arabia

2023· article· en· W4328025162 on OpenAlexvenueno aff
Tawfeeq Alanazi

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessLoyalty business modelMarketingCustomer relationship managementCustomer intelligenceSample (material)LoyaltyCustomer satisfactionGeneral partnershipCustomer retentionPopulationCustomer advocacyCustomer equityCustomer delightCustomer valueService qualityService (business)Sociology

Abstract

fetched live from OpenAlex

The study focused on examining the social customer relationship management impacts on the customer loyalty of five-star hotels in Saudi Arabia. Customer relationship management included the dimensions of customer value, long-term partnership with the customer, customer knowledge, reliance on technology, trust, and social media communication. The study population consists of the customers of five-star hotels in Saudi Arabia. A convenience sample was taken from 500 customers, while the validly retrieved responses were 413. A quantitative approach was conducted in statistical analysis through SPSS and AMOS software. The study demonstrated that social customer relationship management dimensions are impacting the customer loyalty of five-star hotels in Saudi Arabia. Subsequently, the recommendations centered on the establishment of a complaints handling unit to speed up service, identify problems, identify the sites of deficiencies, and take the necessary and appropriate solutions to these problems.

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)0.007
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
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)0.0020.000

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 head0.021
GPT teacher head0.271
Teacher spread0.250 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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