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Record W13789163

The strategic impact of technology based CRM on call centers' performance

2011· article· en· W13789163 on OpenAlexvenueno aff
Aliyu Olayemi Abdullateef, Sany Sanuri Mohd Mokhtar, Rushami Zien Yusoff

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsCenter (category theory)Computer scienceService (business)Product (mathematics)Quality (philosophy)Service qualityCustomer satisfactionStructural equation modelingCustomer serviceCustomer relationship managementKnowledge managementMarketingBusinessProcess managementTelecommunicationsDatabase
DOInot available

Abstract

fetched live from OpenAlex

The primary objective of this paper is to test a model that can explain the impact of technology based CRM on inbound call center performance. To do this, data were collected from 168 call center managers and analyzed through structural equation modeling. The research findings indicate that technology based CRM significantly affects first call resolution and perceived service quality, but weakly influence caller satisfactions through the mediating role of first call resolutions.Observably, this research believes that customer contact centers as the first touch points to company are dependent on other factors such as company policy, product quality, customer characteristics, etc. to influence caller satisfactions, but unfortunately most of these factors fall outside the operational control of contact center activities. The findings in this research has empirically provided the long waiting evidence that technology based CRM applications within the inbound contact center industry can only influence caller satisfactions through first call resolution and perceived service quality. A major implication for call center managers is that this research findings has availed them the opportunity on how to effectively develop, implement, and evaluate their CRM applications.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.042
GPT teacher head0.258
Teacher spread0.216 · 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
Published2011
Admission routes1
Has abstractyes

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