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Record W2883509207 · doi:10.5539/ass.v14n8p76

The Mediating Role of Customers' Satisfaction on the Effect of CRM on Long-Term Customers Loyalty in the Banking Sector in the Palestinian Territory

2018· article· en· W2883509207 on OpenAlexvenueno aff
Raed A. M. Iriqat, Mohannad Abu Daqar

Bibliographic record

VenueAsian Social Science · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessLoyaltyCustomer satisfactionMarketingLoyalty business modelOrder (exchange)Service (business)Competitive advantageService qualityCustomer relationship managementTerm (time)

Abstract

fetched live from OpenAlex

This study aims to investigate the mediating role of customers' satisfaction on the effect of customer relationship management on long-term customers' loyalty in the banking sector in the Palestinian Territory. Using advanced statistical methods. This study supports that there is a high level in implementing the CRM, customers' satisfaction, and long-term customers' loyalty. It showed that these three variables: CRM, customers' satisfaction, and long-term customers' loyalty have a significant role on the Banking sector. CRM and its dimensions, and both of customers' satisfaction, and long-term customers' loyalty are positively significant correlated. Also, finds that there is no role for customers' satisfaction as a mediator variable in enhancing the impact of CRM on long-term customers' loyalty. Moreover, based on SEM the study shows that there is a direct impact of CRM system integration and customers satisfaction on long-term customers' loyalty, whereas there is a direct impact for customers' database and CRM system integration on customers' satisfaction. The scholars find that the Palestinian local banks should pay more efforts to improve their competences to enhance the quality of service and their employees' behavior level. On the other side, they need to keep their customers database updated and to be aligned with the cutting edge technologies to provide better service for customers, which is appropriate and meet their needs by obtaining the accurate information about their preferences in order to build a strong competitive advantage that is hard to imitate, this leads to build a strong relationship with customers.

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)0.007
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.272
Teacher spread0.257 · 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 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

Citations11
Published2018
Admission routes1
Has abstractyes

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