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Record W4200564360 · doi:10.33423/jabe.v23i7.4859

The Effect of the Marketing Mix on Customer Purchase Decision in the Mobile Telecommunication Industry in Sub-Sahara Africa

2021· article· en· W4200564360 on OpenAlexvenueno aff
Emmanuel Selase Asamoah

Bibliographic record

VenueJournal of Applied Business and Economics · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsMarketingBusinessRespondentMobile serviceTelecommunications serviceMarketing strategyService (business)AdvertisingMobile telephonyMobile marketingCustomer advocacyCustomer retentionTelecommunicationsDigital marketingService qualityEngineeringMobile radio

Abstract

fetched live from OpenAlex

This study determined the awareness of the marketing strategies of the mobile telecommunication companies; customers’ purchase decision of products and services provided by the mobile telecommunication companies, and finally the influence of customer awareness of marketing strategies on customer purchase decision. The study adopted a quantitative approach with 300 respondents. The study found that customers of the various mobile telecommunication companies had knowledge of the 7Ps marketing strategies of the mobile service providers. Furthermore, respondent’s intentions to patronize the services of their mobile networks were about the same for all customers. The study also found that customer awareness of the pricing strategy (r=.146, p=.011), product/service strategy (r=.120, p=.038), process strategy (r=.153, p=.008), and promotional strategies (r=.129, p=.025) all have significant positive correlation on customer intention to patronize the services of the telecommunication companies. However, customer awareness of the marketing strategy (marketing mix) played no significant role in predicting the decision of the customer to patronize the services of their mobile telecommunication companies.

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)0.004
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 score0.819
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 integrity0.0000.001
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 head0.012
GPT teacher head0.226
Teacher spread0.213 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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