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Record W4223657144 · doi:10.5430/jms.v13n1p1

Gender: The Moderator Role Between Materialism, Customer Value and Customer Loyality

2022· article· en· W4223657144 on OpenAlexvenueno aff
Eman Abdulhmeed Hasnin, Munirah Sarhan Alqahtani, Somia Abdulkader Othman

Bibliographic record

VenueJournal of Management and Strategy · 2022
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsModerationMaterialismScale (ratio)Likert scaleClothingValue (mathematics)PsychologyHappinessSocial psychologyMarketingAdvertisingBusinessPolitical scienceMathematicsGeographyDevelopmental psychology

Abstract

fetched live from OpenAlex

This research aims to study relationship between materialism, customer value and customer loyalityand test the role of gender as a moderator variable. The questionnaire-measuring used to investigate the relationship between materialism, customer value, and customer loyalty with a moderating role of gender. The scale consists of five Likert scale. After reviewing the theoretical frame, the researcher suggested the use of modifiable, reliable and valid scales for each variable as well as using SPSS version 20 for statistical analysis purposes. The scale to measure the level of materialism. The study indicates that Saudi youth is moving towards awareness and awareness of the brand and seeks to build a personal identity through their favorite brands. This has been helped by the high level of income as it has appeared more among young people than young women. There is also an increasing trend amongst women towards fashion clothing. More than traditional clothes, ownership has also been achieved for them by creating the great sense of happiness that they feel. Materialism has also been linked, to a great extent, to values, customs and traditions, and this is taken into consideration when moving towards materialism.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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 teacher head, 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

Citations0
Published2022
Admission routes1
Has abstractyes

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