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Record W2796539070 · doi:10.5267/j.msl.2018.4.007

Factors influencing business of mobile telecommunication service providers in Vietnam

2018· article· en· W2796539070 on OpenAlexvenueno aff
Ha Thanh Hai, Khong Sin Tan, Yee Yen Yuen

Bibliographic record

VenueManagement Science Letters · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessMobile serviceTelecommunicationsService providerService (business)Mobile telephonyComputer scienceMarketingMobile radio

Abstract

fetched live from OpenAlex

According to the Ministry of Information and Communications in Vietnam, as of November 2015, the number of mobile subscribers is over 120 million ones, accounting for over 86% of the total number of telephone subscribers. With a total population of over 92 million Vietnam citizens, a stable national economy and a large populations of young consumers in the country, mobile communication industries still have a huge potentials for future development. Telecommunication service providers in Vietnam are facing fierce competition. Subscribers are expecting OTT (Over the Top) applications, good quality service and handset subsidy. This study investigated whether legal frameworks, OTT applications, quality of service and handset subsidy are important components of mobile telecommunication service in Vietnam. This study used quantitative method to distribute surveys to mobile subscribers. Findings found that all four factors significantly influence mobile business in Vietnam. Thus, telecommunication service providers in Vietnam must continuously innovate to enhance operational competitiveness, improve business efficiency, expand business scale, and improve its position in the market in order to ensure sustainable development. Moreover, Vietnamese government needs to develop a legal framework to help mobile telecommunication service providers enhance the common interests and benefits of the entire society.

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 score0.018
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.001
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 head0.028
GPT teacher head0.259
Teacher spread0.231 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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