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Record W3105392462 · doi:10.5539/ijms.v12n4p14

Receiving and Action Oriented Attitude of the Youth Towards Mobile Marketing: A Transitional Economy Perspective

2020· article· en· W3105392462 on OpenAlexvenueno aff
Mustapha Iddrisu, Akolaa Andrews Adugudaa, Albert Martins

Bibliographic record

VenueInternational Journal of Marketing Studies · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsMarketingExploratory researchMobile marketingBusinessAdvertisingMarketing mixAction (physics)Perspective (graphical)Digital marketingSociologySocial science

Abstract

fetched live from OpenAlex

The advancement in technology is influencing the ways in which organizations conduct business and marketing activities. Mobile Marketing has become one of the most widespread media to communicate with potential and existing customers mainly in the form of text advertisements through the internet. This study is aimed at finding out the receiving and action-oriented attitudes of the youth towards mobile marketing, particularly the rate at which the youth use mobile marketing to determine the propensity to be influenced in their actions. The study also investigated the factors affecting consumer attitudes and their relationship with mobile marketing. The study employed descriptive and exploratory research methodology design and the data were collected using a structured questionnaire. Four hundred (400) questionnaires were administered to young people between the ages of 18 and 35 years in Accra and Three hundred and fifty 350 were used. We found a high rate of youth’s preference for mobile marketing messages and a relationship between youth attitude and mobile marketing messages and/or the youth’s attitude being influenced by mobile marketing messages. Finally, it revealed that there is a relationship between the youth’s receiving and action-oriented attitude and consumer factors that influence the use of mobile marketing.

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)0.005
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.013
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 head0.139
GPT teacher head0.413
Teacher spread0.274 · 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.

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
Published2020
Admission routes1
Has abstractyes

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