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Record W2148307644 · doi:10.5267/j.msl.2012.10.004

An investigation on role of customer relationship management (CRM) in increasing organizational effectiveness

2012· article· en· W2148307644 on OpenAlexvenueno aff
Mohammad Hakkak, Mahmoud Reza Esmaeili, Moslem Mirzaei

Bibliographic record

VenueManagement Science Letters · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaRanking (information retrieval)Customer satisfactionTest (biology)MarketingCustomer relationship managementSample (material)BusinessLoyaltyLoyalty business modelPsychologyCustomer retentionKnowledge managementComputer scienceService qualityService (business)Artificial intelligence

Abstract

fetched live from OpenAlex

This paper investigates the role of customer relationship management (CRM) in improving organizational effectiveness.The proposed model of this paper is implemented on customers of one of Iranian banks called Keshavarzi (Agriculture).In this research a questionnaire was prepared and 150 customers as sample were selected randomly.The results of the survey have been validated using Cronbach alpha, which was 0.926.Research hypotheses are analyzed using Pearson's correlation test and all hypotheses are confirmed when the level of significance was five percent.The results of our survey have disclosed that all these mentioned factors could impact customer satisfaction, positively.In addition, we have also considered Freedman test to rank the relative importance of these factors, results indicate that customer-centered was the first priority followed by recognizing customers' need, Mutual understanding and Loyalty.Customer complaints are also the last priority in the ranking.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.248
Teacher spread0.226 · 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 designQualitative
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

Citations2
Published2012
Admission routes1
Has abstractyes

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