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Record W1540122326 · doi:10.5539/ibr.v8n5p230

The Effect of eCRM Practices on eWOM on Banks’ SNSs: The Mediating Role of Customer Satisfaction

2015· article· en· W1540122326 on OpenAlexvenueno aff
Abdallah Q. Bataineh

Bibliographic record

VenueInternational Business Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
FundersApplied Science Private University
KeywordsDatabase transactionInterpersonal communicationBusinessCustomer satisfactionSample (material)Affect (linguistics)PsychologyTest (biology)MarketingComputer scienceSocial psychologyDatabase

Abstract

fetched live from OpenAlex

The main objective of this research is to examine the effect of electronic customer relationship management (eCRM) practices provided by banks operating in Jordan on electronic word of mouth (eWOM) on these banks social networking sites (SNSs) namely Facobook, Twitter and Instagram. Throughout a comprehensive reviewing of literatures in this growing area; the researcher built up a research model to describe the relationships between the research independent, dependent and mediating variables. Moreover, well structured questionnaire has been used to gather data from the research sample which consisted of 507 customers, who have had an e-transaction with their banks, and have active accounts on one or more of the previous SNSs. Accordingly, multiple regression test was used to analyze gathered data; the results showed that electronic direct mail, perceived rewards and interpersonal communication are respectively affect eWOM of banks SNSs. What is more, the mediating role of customer satisfaction was supported. Hence, conclusions, managerial implications and suggestions for upcoming researches are also provided.

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)0.008
metaresearch head score (Gemma)0.057
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.079
GPT teacher head0.451
Teacher spread0.372 · 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.

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

Citations18
Published2015
Admission routes1
Has abstractyes

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