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Record W3151784989 · doi:10.5267/j.uscm.2021.3.003

The effect of CRM on employee performance in banking industry

2021· article· en· W3151784989 on OpenAlexvenueno aff
Lis M. Yapanto, Ahyar Muhammad Diah, Kannapat Kankaew, Anita Kusuma Dewi, William Dextre-Martínez, Ardhariksa Zukhruf Kurniullah, Luis Augusto Villanueva Benites

Bibliographic record

VenueUncertain Supply Chain Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessMarketingCustomer relationship managementLoyaltyProfit (economics)Customer satisfactionLoyalty business modelCustomer retentionMultinational corporationGeneral partnershipService (business)Service qualityEconomicsFinance

Abstract

fetched live from OpenAlex

The relationship between the organization and its clients is the life of every enterprise, whether it is a multinational corporation of several billion employees and a multi-million-deposit business or sole traders with a handful of daily customers. The relationship between the organization and its traditions is the key concern. Between these two cases, consumer relationship management (CRM) is the same in theory and may differ significantly. Both the company and consumers have some factors to meet, such as the desires and expectations of all sides, before forming a contract. We need to earn a profit to succeed and to improve clients expect excellent support, better goods and reasonable pricing. The implementation of a CRM program will impact consumer service and customer knowledge for various purposes. Likewise, adopting a CRM strategy would definitely affect consumer loyalty and awareness. CRM guarantees that consumers are happy and strengthens ties between the company and its clients. Such practices improve the partnership between customers and sales representatives. The study carried out the quantitative approach in the delivery of the questionnaire to more than 100 bank customers. In concise and inferential statistics, the data were handled using the SPSS statistical method. Data indicates that the strong relationship between consumer loyalty and customer happiness of CRM technologies occurs and the stronger the overall customer satisfaction score, the larger the volume of CRM technology deployed.

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)0.002
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 score0.293
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.248
Teacher spread0.232 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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