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Record W3039735735 · doi:10.5267/j.msl.2020.6.043

The role of customer relationship management success factors on enhancing the mental image of telecommunications companies in Jordan

2020· article· en· W3039735735 on OpenAlexvenueno aff
Mohammad Nassar D. Almarshad, Salameh S. Al-Nawafah, Mujahed Hani A. Al Tahrawi

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessTelecommunicationsMarketingComputer science

Abstract

fetched live from OpenAlex

This is one of the first studies of customer relationship management (CRM) in an emerging market of Telecommunications Companies in Jordan. The purpose of this study is to investigate the influence of customer relationship management (CRM) success factors on the mental image among a sample of Jordanian Telecommunications Companies customers. A review of the literature relating to CRM and mental image in both developed and emerging markets was undertaken. The variables which were chosen formed the overall success factors of CRM which are (profitability, knowledgeability, loyalty, attitude, and satisfaction) for enhancing the mental image of telecommunications companies in Jordan. The study sample was selected randomly with a total of 340 citizens by using a quantitative method (questionnaire) to collect data. A 64 percent response rate was achieved. The results revealed the sample held a positive attitude towards the main hypothesis of the study. The variable testing indicated that the most influential variable of the aforementioned on the mental image of the organization appeared through the analysis to be employee attitude as the most influential while profitability appeared to be the least influential variable of all. The researchers recommend the start giving enough attention to the success factors.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.235
Teacher spread0.221 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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