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Record W2530323251 · doi:10.1504/ijmc.2016.077325

The influence of switching costs and satisfaction on loyalty towards smartphone service providers

2016· article· en· W2530323251 on OpenAlexaff
Cristina Calvo-Porral, Jean Pierre Lévy Mangin

Bibliographic record

VenueInternational Journal of Mobile Communications · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsBusinessMarketingCustomer satisfactionLoyaltyService providerLoyalty business modelCustomer retentionService (business)Structural equation modelingCustomer advocacyService qualityAdvertisingComputer science

Abstract

fetched live from OpenAlex

Introduction: With the rapid development of communication technologies and the introduction of smartphones in the marketplace, the industry of mobile phone communication services is making great efforts to attract new customers and retain current ones. Purpose: This study aims to analyse how the switching costs and customer satisfaction influence customers' loyalty towards the smartphone service provides. Additionally, the mediating role of customer loyalty is also examined. Methodology: For this purpose, drawing on a sample of 370 customers, an analysis was developed through structural equation modelling. Findings: Our findings suggest that customer satisfaction with their smartphone services is determined by two key variables - service value and corporate image - while switching costs exert a low influence on customer satisfaction. Implications: Considering that our findings highlight low switching costs, service providers should focus their marketing efforts towards attracting new customers and increasing primary demand, rather than retaining their existing customers.

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 score0.841
Threshold uncertainty score0.256

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 communication0.0000.001
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 head0.023
GPT teacher head0.293
Teacher spread0.270 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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