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Record W2183140114

User Satisfaction with Mobile Services in Canada

2004· article· en· W2183140114 on OpenAlexaffabout
Ofir Turel, Alexander Serenko

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLoyaltyCustomer satisfactionBusinessBenchmarkingMarketingMobile phoneService providerLoyalty business modelService (business)TelecommunicationsIndex (typography)Telecommunications serviceService qualityComputer science
DOInot available

Abstract

fetched live from OpenAlex

Abstract: While satisfaction and loyalty in regards to physical goods and some services have been studied to a great extent in marketing and information systems research, there is little research on these factors with respect to mobile telecommunications services. Furthermore, there is no standard measure for satisfaction with these services. This study taps into these voids and examines the antecedents of satisfaction and loyalty through an empirical study of 80 cellular subscribers in Ontario, Canada. Results of the study suggest that most causal relationships depicted by the American Customer Satisfaction Model are valid in the mobile telecommunications sector. However, due to the switching barrier, loyalty to a wireless service provider is no longer a unidimensional construct, but rather comprised of two independent factors – repurchase likelihood and price tolerance. This investigation also suggests that there are some differences in service perceptions between prepaid and post-paid cell phone users. Based on the model, a satisfaction index is calculated for Canadian wireless service providers. This index is found to be low in comparison to those of other sectors and industries. Overall, this study forms the foundations for future benchmarking of the performance of wireless network operators in terms of satisfaction and loyalty.

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)0.000
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 score0.026
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.007
GPT teacher head0.201
Teacher spread0.193 · 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

Citations15
Published2004
Admission routes2
Has abstractyes

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